Xenomorph Blog - Financial Markets Industry

Posts categorized "Financial Markets Industry"

PRMIA - Operational Risk, Big Data and Human Behaviour

I attended Challenges and Innovations in Operational Risk Management event last night which was surprisingly interesting. I say surprising since I must admit to some prejudice against learning about operational risk, which has for me the unfortunate historical reputation of being on the dull side.

Definition of Operational Risk

Michael Duffy (IBM GRC Strategy Leader, Ex-CEO of OpenPages) was asked by the moderator to define Operational Risk. Michael answered that he assumed that most folks attending already knew the definition (fair comment, the auditorium was full of risk managers...), but he sees it in practice as the definition of policy, the controls to enforce the policies and ongoing monitoring of the performance of the controls. Michael suggestion that many where looking to move the scope and remit of Operational Risk into business performance improvement, but clients are not there yet on this more advanced aspect.

Vick Panwar (Financial Services Industry Lead, SAS) added that Operational Risk was there to mitigate the risks for those unexpected future events (getting into the territory of Dick Cheney's Unknown Unknowns which I never tire of, particularly after a glass of wine).

Rajeev Lakra (Director Operational Risk Management, GE Treasury) took his definition from Basel II of Operational Risk as risk of loss resulting from inadequate or failed internal processes, people and systems, or from external events. Coming from GE, he said that he thought of best practice Operational Risk as similar to another GE initiative in the use of Six Sigma for improving process management. Raj said that his operational risks were mainly concerned with trade execution so covering data quality/errors, human error and settlement errors.

Beyond Box Ticking for Operational Risk

Raj said that Operational Risk is treated seriously at GE with the Head of Operational Risk reporting into the CRO and leaders of Operational Risk in each business division.

Michael suggested that the "regulators force us to do it" motive for Operational Risk had reduced given some of the operational failures during the financial crisis and recent "rogue trader" events, with the majority of institutions post-2008 having created risk committees at the "C" level and being so much more aware of tail events and the reputational damage that can damage shareholder value.

Vik said that Operational Risk is concerned primarily with "tail events" which by definition are not limited in size and therefore should be treated seriously. Pragmatically, he suggested that "the regulators need it" should be used as an excuse if there was no other way to get people to pay attention, but getting them to understand the importance of it was far more powerful.

The "What's in it for you" Approach to Operational Risk

Raj emphasised that it was possible to emphasise the benefits of operational risk to people in their everyday jobs, explaining to operators/managers that if they get frustated with failures/problems in the working day, then wouldn't it be great if these problems/losses were recorded so that they could justify a process change to senior management. He emphasised that this was a big cultural challange at GE.

Michael suggested that his clients in financial markets had gone through risk assessment, controls and recording of losses, but had not yet progressed to the use of Operational Risk to improve business performance.

Duplication of Effort

A key thing that all the panelists discussed was the overlap at many organisations between Operational Risk, Audit and Compliance. The said that the testing of the controls used for each had much in overlap, but was not based on a common nomenclature nor on common systems. For instance Vik pointed out that many of the tests on controls in Sarbanes-Oxley compliance were re-usable in an Operational Risk context, but that this was not yet happening. Vik said that this pointed to the need for comprehensive GRC platform rather than many siloed platforms.

Michael said that regulators want an integrated view, but no institution has an integrated nomenclature as yet. He recounted that one client sent 12 different control tests to branches that needed to be filled in for head office, which was a waste of resources and confusing/demotivating for staff. Raj said that the integration of Audit and Operational Risk at GE had proved to be a very difficult process. All agreed that senior management need to get involved and that a 5 year vision of how things should be incrementally integrated needs to be put in place.

Audience Questions:

Is business process risk different to business product risk? Michael said that Operational Risk certainly does and should cover both internal process and also the risks produced by the introduction of a new financial product for instance (is it well understood for instance, do clients understand what they are being sold?). He added that Operational Risk encompassed both the quantitative (statistical number of failures for instance) and the qualitative for which statistics were either not available (or not relevant to the risk).

Are there any surrogate measures for Operational Risk? Here a member of the audience was relaying senior management comments and frustration over the stereotyped red/amber/green traffic lights approach to reporting on operational risk. Michael mentioned the Operational Riskdata eXchange Association (ORX) where a number of financial institutions anonymously share operational risk loss data with a view to using this data to build better models and measures of operational risk. Apparently this has been going on since 2003 and the participants already have a shared taxonomy for Operational Risk. (my only comment on having a single measure for "operational riskiness" is that do you really want a "single number" approach to make things simple for C-level managers to understand, or should the C-levels be willing to understand more of the detail behind the number?)

Is "Rogue Trading" Operational Risk? Michael said that it definitely was, and that obviously each institution must control and monitor its trading policies to ensure they were being followed. The panel proposed that Operational Risk applied to trading activity could be a good application of "Big Data" (much hyped by industry journalists lately) to understand typical trading patterns and understand unusual trading patterns and behaviours. (Outside of bulk tick-data analysis this is one of the first sensible applications of Big Data so far that I have heard suggested so far given how much journalists seem to be in love with the "bigness" of it all without any business context to why you actually would invest in it...sorry, mini-rant there for a moment...)

Summary

Good event with an interesting panel, the GE speaker had lots of practical insight and the vendor speakers were knowledgeable without towing the marketing line too much. Operational Risk seems to be growing up in its linkage into and across market, credit and liquidity risk. The panel agreed however that it was very early days for the discipline and a lot more needs to be done.

Given the role of human behaviour in all aspects of the recent financial crisis, then in my view Operational Risk has a lot to offer but also a lot to learn, not least in that I think it should market itself more agressively along the lines of being the field of risk management that encompasses the study and understanding of human behaviour. Maybe there is a new career path looming for anthropologists in financial risk management...

Posted by Brian Sentance | 27 January 2012 | 11:30 pm

The Volcker Rule - aka one man's trade is another man's hedge

One of the PRMIA folks in New York kindly recommended this paper on the Volcker Rule, in which Darrell Duffie criticises the proposed this new US regulation design to drastically reduce proprietary ("own account") trading at banks.

As with all complex systems like financial markets, the more prescriptive the regulations become the harder it is "lock down" the principles that were originally intended. In this case the rules (due July 2012) make an exception to the proprietary trading ban where the bank is involved in "market-making", but Darrell suggests that the basis for what types of trades are "market-making" and what types of trades are more pure "proprietary trading" are problematic in this case, as there will always be trades that are part of "market-making" process (i.e. providing immediacy of execution to customers) that are not directly and immediately associated with actual customer trading requests.

He suggests that the consequences of the Volcker Rule as it is currently drafted will be higher bid-offer spreads, higher financing costs and reduced liquidity in the short-term, and a movement of liquidity to unregulated entities in the medium term possibly further increasing systemic risk rather than reducing it. Seems like another example of "one man's trade is another man's hedge" combined with "the law of unintended consequences". The latter law doesn't give me a lot of confidence about the Dodd-Frank regulations (of which the Volcker Rule forms part), 2319 pages of regulation probably have a lot more unintended consequences to come.

Posted by Brian Sentance | 20 January 2012 | 3:47 pm

In quiet praise of introverts

Corporate (and social) America does lots of things very well - positiveness, enthusiam and lack of (English?) cynicism being some of the best attributes in my view - but other things are not so good such as long "townhall" conference calls with 30 people on the call and only 3 people taking part, and the seeming need to continue talking when it is already evident to you and many listening that you don't know what you are talking about. With these things in mind, I think this article "The Rise of the New Groupthink" in the New York Times is worth a read, as it challenges some of the mainstream practices on corporate collaboration and teaming, and comes out in quiet praise of the creative power of introverts. Seems like Dilbert's cubicle still has its merits in these days of open plan offices and desk sharing.

Posted by Brian Sentance | 19 January 2012 | 8:22 pm

The financial crisis and Andrew Lo's reading list

I spotted this in the FT recently - for those of you diligent enough to want to read more about the possible causes and possible solutions to the (ongoing) financial crisis, then Andrew Lo may have saved us all a lot of time in his 21-book review of the financial crisis. Andrew reviews 10 books by academics, 10 by journalists and one by former Treasury Secretary Henry Paulson.

Andrew finds a wide range of opinions on the causes and solutions to the crisis, which I guess in part reflects that regardless of the economic/technical causes, human nature is both at the heart of the crisis and evidently also at the heart of its analysis. He regards the differences in opinion quite healthy in that they will be a catalyst for more research and investigation. I also like the way Andrew starts his review with a description of how people's view of the same events they have lived through can be entirely different, something that I have always found interesting (and difficult!).

A quote from Napolean (that I am in danger of over-using) seems appropriate to Andrew's review: "History is the version of past events that people have decided to agree upon" but maybe Churchill wins in this context with: "History will be kind to me for I intend to write it.". Maybe we should all get writing now before it is too late...

Posted by Brian Sentance | 18 January 2012 | 11:17 pm

Latest from the EDM Council

Click here for an executive summary of what the EDM Council is up to on regulation, LEI and the Semantics Repository etc. Due credit to the Council for getting Bloomberg on board - sounds increasingly like Bloomberg may have decided to treat the topic seriously as opposed to assuming having a terminal solves everything.

Posted by Brian Sentance | 13 January 2012 | 4:24 pm

Pandit on Comparing Apples and Risk

For someone who has been criticised a lot over recent years, Vikram Pandit CEO of Citigroup, seems to have come up with an interesting risk management idea in his latest article in the FT. Vikram proposes that regulators put together an standard, multi-asset "benchmark" portfolio that all financial institutions would have to provide risk numbers on, enabling regulators to understand more of the risk management capabilities of each institution and avoiding any detailed disclosure of the portfolio actually held by each firm.

I guess a key thing would be that such numbers would have to be disclosed to the regulator away from public view, since we all know that otherwise the numbers would converge and all the banks would be doing the same thing (or at least copying each other's numbers?). Reminds me of a great talk at the RiskMinds event a few years back, praising diversity of approach and criticising regulators for effectively forcing everyone to do the same thing.

Posted by Brian Sentance | 12 January 2012 | 2:34 pm

FaceBank

Thought-provoking post by Alex Bray on finextra.com, about how internet banking sites are becoming outdated just like physical "branches" of banks did, and how they need to integrate more tightly with social networking sites (what doesn't these days?). The power of the network continues to rise, and it seems like FaceBank is becoming a reality (see the first part of my "tongue in cheek" Wilmott article from a few years back).

Posted by Brian Sentance | 11 January 2012 | 6:21 pm

Tax makes you richer? It will never happen (unfortunately).

Despite all of the world's economic woes at the moment, it is great to see that there a few people who are still able to "think different" on ways to get out of the mess we are in. For example, I came across Hassan Heikal's article in the FT recently, in which he suggests a one-off ten percent plus wealth tax on all those individuals world-wide with wealth above $10million.

This windfall tax would then be used to address the global government debt crisis in one fell swoop. Not necessarily the most obviously innovative idea you will have ever heard, until you consider that as a result of stabilising the crisis that the remaining 90% of the assets still held by the wealthy individuals would post-crisis jump in market value, and most likely become worth significantly more than the value of the 100% pre-crisis.

So everybody wins and nobody looses, so long as they all do it. Which, being individuals, they won't. Unfortunately. I guess it is a reminder that we shouldn't look further than ourselves and good old human behaviour for how we got into this mess, and indeed how we are going to get out of it too.

Best wishes for a great holiday break, a great family Christmas for those of you that celebrate it, and here's to an austerity-breaking 2012 (we all need to keep the faith...).

Posted by Brian Sentance | 16 December 2011 | 10:46 pm

PRMIA - From Risk Measurement to Risk Management by Samuel Won

I attended the PRMIA event last night "Risk Year in Review" at Moody's New York offices. It was a good event, but by far the most interesting topic of the evening for me was from Samuel Won, who gave a talk about some of the best and most innovative risk management techniques being used in the market today. Sam said that he was inspired to do this after reading the book "The Information" by James Gleik about the history of information and its current exponential growth. Below are some of the notes I took on Sam's talk, please accept my apologies in advance for any errors but hopefully the main themes are accurate.

Early '80s ALM - Sam gave some context to risk management as a profession through his own personal experiences. He started work in the early 80's at a supra-regional bank, managing interest rate risk on a long portfolio of mortgages. These were the days before the role of "risk manager" was formally defined, and really revolved around Asset and Liability Management (ALM).

Savings and Loans Crisis - Sam then changed roles and had some first hand experience in sorting out the Savings and Loans crisis of the mid '80s. In this role he become more experienced with products such as mortgage backed securities, and more familiar with some of the more data intensive processes needed to manage such products in order to account for such factors such as prepayment risk, convexity and cashflow mapping.

The Front Office of the '90s - In the '90s he worked in the front office at a couple of tier one investment banks, where the role was more of optimal allocation of available balance sheet rather than "risk management" in the traditional sense. In order to do this better, Sam approached the head of trading for budget to improve and systemise this balance sheet allocation but was questioned as to why he needed budget when the central Risk Control department had a large staff and large budget already.

Eventually, he successfully argued the case that Risk Control were involved in risk measurement and control, whereas what he wanted to implement was active decision support to improve P&L and reduce risk. He was given a total budget of just $5M (small for a big bank) and told to get on with it. These two themes of implementing active decision support (not just risk measurement) and have a profit motive driving better risk management ran through the rest of his talk.

A Datawarehouse for End-Users Too - With a small team and a small budget, Sam made use of postgraduate students to leverage what his team could develop. They had seen that (at the time) getting systems talking to each other was costly and unproductive, and decided as a result to implement a datawarehouse for the front office, implementing data normalisation and data scrubbing, with data dashboard over the top that was easy enough for business users to do data mining. Sam made the point that useability was key in allowing the business people to extract full value from the solution.

Sam said that the techniques used by his team and the developers were not necessarily that new, things like regression and correlation analysis were used at first. These were used to establish key variables/factors, with a view to establish key risk and investment triggers in as near to real-time as possible. The expense of all of this development work was justified through its effects on P&L which given its success resulting in more funding from the business.

Poor Sell-Side Risk Innovation - Sam has seen the most innovative risk techniques being used on the buy-side and was disappointed by the lack of innovation in risk management at the banks. He listed the following sell-side problems for risk innovation:

politically driven requirements, not economically driven
arbitrary increases in capital levels required is not a rigorous approach
no need for decision analysis with risk processes
just passing a test mentality
just do the marginal work needed to meet the new rules
no P&L justification driving risk management

Features of Innovative Approaches - Sam said that he had noted a few key features of some of the initiatives he admired at some of the asset managers:

Based on a sophisticated data warehouse (not usually Oracle or Sybase, but Microsoft and other databases used - maybe driven by ease of use or cost maybe?)
Traders/Portfolio Managers are the people using the system and implementing it, not the technical staff.
Dedicated teams within the trading division to support this, so not relying on central data team.

A Forward-Looking Risk Model Example - The typical output from such decision analysis systems he found was in the form of scenarios for users to consider. A specific example was a portfolio manager involved in event-driven long-short equity strategies around mergers and acquisitions. The manager is interested in the risk that a particular deal breaks, and in this case techniques such as Value at Risk (VaR) do not work, since the arbitrage usually requires going long the company being acquired and short the acquiror (VaR would indicate little risk in this long-short case). The manager implemented a forward looking model that was based on information relevant to the deal in question plus information from similar historic deals. The probabilities used in the model where gathered from a range of sources, and techniques such as triangulation where used to verify the probabilities. Sam views that forward-looking models to assist in decision support are real risk management, as opposed to the backward-looking risk measurement models implemented at banks to support regulatory reporting.

Summary - Sam was a great speaker, and for a change it was refreshing to not have presentation slides backing up what the speaker was saying. His thoughts on forward looking models being true risk management and moving away from risk measurement seem to echo those of Ricardo Rebanato of a few years back at RiskMinds (see post). I think his thoughts on P&L motivation being the only way that risk management advances are correct, although I think there is a lot of risk innovation at the banks but at a trading desk level and not at the firm-wide level which is caught up in regulation - the trading desks know that capital is scarce and are wanting to use it better. I think this siloed risk management flies in the face of much of the firm-wide risk management and indeed firm-wide data management talked about in the industry, and potentially still shows that we have a long way to go in getting innovation and forward looking risk management at a firm level, particularly when it is dominated by regulatory requirements. However, having a truly integrated risk data platform is something of a hobby-horse for me, I think it is the foundation for answering all of the regulatory and risk requirementst to come, whatever their form. Finally, I could not agree more easy analysis for end-users is a vital part of data management for risk, allowing business users to do risk management better. Too many times IT is focussed on systems that require more IT involvement, when the IT investment and focus should be on systems that enable business users (trading, risk, compliance) to do more for themselves. Data management for risk is key area for improvement in the industry, where many risk management sytem vendors assume that the world of data they require is perfect. Ask any risk manager - the world of data is not perfect and manual data validation continues to be a task that takes time away from actually doing risk management.

Posted by Brian Sentance | 14 December 2011 | 11:29 pm

A-Team event – Data Management for Risk, Analytics and Valuations

My colleagues Joanna Tydeman and Matthew Skinner attended the A-Team Group's Data Management for Risk, Analytics and Valuations event today in London. Here are some of Joanna's notes from the day:

Introductory discussion

Andrew Delaney, Amir Halton (Oracle)

Drivers of the data management problem – regulation and performance.

Key challenges that are faced – the complexity of the instruments is growing, managing data across different geographies, increase in M&As because of volatile market, broader distribution of data and analytics required etc. It’s a work in progress but there is appetite for change. A lot of emphasis is now on OTC derivatives (this was echoed at a CityIQ event earlier this month as well).

Having an LEI is becoming standard, but has its problems (e.g. China has already said it wants its own LEI which defeats the object). This was picked up as one of the main topics by a number of people in discussions after the event, seeming to justify some of the journalistic over-exposure to LEI as the "silver bullet" to solve everyone's counterparty risk problems.

Expressed the need for real time data warehousing and integrated analytics (a familiar topic for Xenomorph!) – analytics now need to reflect reality and to be updated as the data is running - coined as ‘analytics at the speed of thought’ by Amir. Hadoop was mentioned quite a lot during the conference, also NoSQL which is unsurprising from Oracle given their recent move into this tech (see post - a very interesting move given Oracle's relational foundations and history)

Impact of regulations on Enterprise Data Management requirements

Virginie O’Shea, Selwyn Blair-Ford (FRS Global), Matthew Cox (BNY Melon), Irving Henry (BBA), Chris Johnson (HSBC SS)

Discussed the new regulations, how there is now a need to change practice as regulators want to see your positions immediately. Pricing accuracy was mentioned as very important so that valuations are accurate.

Again, said how important it is to establish which areas need to be worked on and make the changes. Firms are still working on a micro level, need a macro level. It was discussed that good reasons are required to persuade management to allocate a budget for infrastructure change. This takes preparation and involving the right people.

Items that panellists considered should be on the priority list for next year were:

· Reporting – needs to be reliable and meaningful

· Long term forecasts – organisations should look ahead and anticipate where future problems could crop up.

· Engage more closely with Europe (I guess we all want the sovereign crisis behind us!)

· Commitment of firm to put enough resource into data access and reporting including on an ad hoc basis (the need for ad hoc was mentioned in another session as well).

Technology challenges of building an enterprise management infrastructure

Virginie O’Shea, Colin Gibson (RBS), Sally Hinds (Reuters), Chris Thompson (Mizuho), Victoria Stahley (RBC)

Coverage and reporting were mentioned as the biggest challenges.

Front office used to be more real time, back office used to handle the reference data, now the two must meet. There is a real requirement for consistency, front office and risk need the same data so that they arrive to the same conclusions.

Money needs to be spent in the right way and fims need to build for the future. There is real pressure for cost efficiency and for doing more for less. Discussed that timelines should perhaps be longer so that a good job can be done, but there should be shorter milestones to keep business happy.

Panellists described the next pain points/challenges that firms are likely to face as:

· Consistency of data including transaction data.

· Data coverage.

· Bringing together data silos, knowing where data is from and how to fix it.

· Getting someone to manage the project and uncover problems (which may be a bit scary, but problems are required in order to get funding).

· Don’t underestimate the challenges of using new systems.

Better business agility through data-driven analytics

Stuart Grant, Sybase

Discussed Event Stream Processing, that now analytics need to be carried out whilst data is running, not when it is standing still. This was also mentioned during other sessions, so seems to be a hot topic.

Mentioned that the buy side’s challenge is that their core competency is not IT. Now with cloud computing they are more easily able to outsource. He mentioned that buy side shouldn’t necessarily build in order to come up with a different, original solution.

Data collection, normalisation and orchestration for risk management

Andrew Delaney, Valerie Bannert-Thurner (FTEN), Michael Coleman (Hyper Rig), David Priestley (CubeLogic), Simon Tweddle (Mizuho)

Complexity of the problem is the main hindrance. When problems are small, it is hard for them to get budget so they have to wait for problems to get big – which is obviously not the best place to start from.

There is now a change in behaviour of senior front office management – now they want reports, they want a global view. Front office do in fact care about risk because they don’t want to lose money. Now we need an open dialogue between front office and risk as to what is required.

Integrating data for high compute enterprise analytics

Andrew Delaney, Stuart Grant (Sybase), Paul Johnstone (independent), Colin Rickard (DataFlux)

The need for granularity and transparency are only just being recognised by regulators. The amount of data is an overwhelming problem for regulators, not just financial institutions.

Discussed how OTCs should be treated more like exchange-traded instruments – need to look at them as structured data.

Posted by Brian Sentance | 17 October 2011 | 11:44 pm

Internal model approval, risk management and regulatory compliance

Achieving regulatory approval can be challenging if we consider that regulators are concerned about both the risk calculation methodology in place but also the quality, consistency and auditability of the data feeding the risk systems used for regulatory reporting.

The data management project at LBBW (Landesbank Baden-Württemberg), for example, was initiated to support LBBW’s internal model for market risk calculations, combined with the additional aim of enabling risk, back office and accountancy departments to have transparent access to high quality and consistent data.

This required a consolidated approach to the management of data in order to support future business plans and successful growth and we worked with LBBW to provide a centralised analytics and data management platform which could enhance risk management, deliver validated market data based upon consistent validation processes and ensure regulatory compliance.

More information on the joint project at LBBW can be found in the case study, available on our website. Any questions, drop us a line!

Posted by Sara Verri | 22 September 2011 | 6:21 pm

Data Unification - just when you thought it was safe to go back in the water...

Sitting by the sea, you have just finished your MATLAB reading and now are wondering what to read next?

No worries!

We have just published our "TimeScape Data Unification" white paper. Not a pocket edition I am afraid, but some of you may find it interesting.

It describes how - post-crisis - a key business and technical challenge for many large financial institutions is to knit together their many disparate data sources, databases and systems into one consistent framework than can meet the ongoing demands of the business, its clients and regulators. It then analyses the approaches that financial institutions have adopted to respond to this issue, such as implementing a ETL-type infrastructure or a traditional golden copy data management solution.

Taking on from their effectiveness and constraints, it then shows how companies looking to satisfy the need for business-user access to data across multyple systems should consider a "distributed golden copy" approach. This federated approach deals with disparate and distributed sources of data and should also provide easy and end-user interactivity whilst maintaining data quality and auditability.

The white paper is available here if you want to take a look and if you have any feedback or questions, drop us a line!

Posted by Sara Verri | 27 July 2011 | 3:19 pm

MATLAB - The perfect read for the beach...

For those who are wondering what summer reading to take on holiday, we have just published our white paper "TimeScape and MATLAB", a pocket edition which outlines how TimeScape and MATLAB can be combined to provide enhanced data analysis and visualisation tools to financial organisations.

Whilst swimming in the blue ocean, walking in the countryside or enjoying a new country, take a break and find out how TimeScape's best of breed data capture and storage can be combined with the analytical capabilities of MATLAB to produce compelling solutions to real-world problems encountered within financial services.

Ok, ok, kidding here. Just go on holiday and enjoy your time off from complex financial problems!

But when you are back or if you are very interested (or sadly not going on holiday soon), please take a look at our white paper. It details how:

TimeScape data and analytics can be accessed from MATLAB
MATLAB computational and visualization tools can be used to manipulate and analyse TimeScape data
Complex data sets generated in MATLAB can be saved back to TimeScape for persisted storage
MATLAB components can be called from TimeScape to enrich TimeScape hosted functionality

and much more.

Feel also free to suggest this summer reading to your friends (or enemies!).

Posted by Sara Verri | 22 July 2011 | 2:40 pm

PRMIA on Data and Analytics

Final presentation at the PRMIA event yesterday was by Clifford Rossi and was entitled "The Brave New World of Data & Analytics Following the Crisis: A Risk Manager's Perspective".

Clifford got his presentation going with a humorous and self-depricating start by suggesting that his past employment history could in fact be the missing "leading indicator" for predicting orgnisations in crisis, having worked at CitiGroup, WaMu, Countrywide, Freddie Mac and Fannie Mae. One of the other professors present said that he didn't do the same to academia (University of Maryland beware maybe!).

Clifford said that the crisis had laid bare the inadequacy and underinvestment in data and risk technology in the financial services sector. He suggested that the OFR had the potential to be a game changer in correcting this issue and in helping the role of CRO to gain in stature.

He gave an example of a project at one of the GSEs he had worked at called "Project Enterprise" which was to replace 40 year old mainframe based systems (systems that for instance only had 3 digits to identify a transaction). He said that he noted that this project had recently been killed, having cost around $500M. With history like this, it is not surprising that enterpring risk data warehousing capabilities were viewed as black holes without much payoff prior to the crisis. In fact it was only due to Basel that data management projects in risk received any attention from senior management in his view.

During the recent stress test process (SCAP) the regulators found just how woeful these systems were as the banks struggled to produce the scenario results in a timely manner. Clifford said that many banks struggled to produce a consistent view of risk even for one asset type, and that in many cases, corporate acquisitions had exascerbated this lack of consistency in obtaining accurate, timely exposure data. He said that the mortgage processing fiasco showed the inadequacy of these types of systems (echoing something I heard at another event about mortgage tagging information being completely "free-fromat", without even designated fields for "City" and "State" for instance)

Data integrity was another key issue that Clifford discussed, here talking about the lack of historical performance data leading to myopia in dealing with new products and poor defintions of product leading to risk assessments based on the originator rather than on the characteristics of the product. (side note: I remember prior to the crisis the credit derivatives department at one UK bank requisitioning all new server hardware to price new CDO squared deals given it was supposedly so profitable, it was at that point that maybe I should have known something was brewing...) Clifford also outlined some further data challenges, such as the changing statistical relationship between Debt to Income ratio and mortgage defaults once incomes were self-declared on mortgages.

Moving on to consider analytics and models, Clifford outlined a lot of the concerns covered by the Modeller's Manifesto, such as the lack of qualitative judgement and over-reliance on the quantitative, efficiency and automation superceding risk management, limited capability to stress test on a regular basis, regime change, poor model validation, and cognitive biases reinforced by backward-looking statistical analysis. He made the additional point that in relation to the OFR, they should concentrate on getting good data in place before spending resource on building models.

In terms of focus going forward, Clifford said the liquidity, counterparty and credit risk management were not well understood. Possibly echoing Ricardo Rebonato's ideas, he suggested that leading indicators need to be integrated into risk modelling to provide the early warning systems we need. He advocated that the was more to do on integrating risk views across lines of business, counterparties and between the banking and trading book.

Whilst being a proponent of the OFRs potential to mandate better Analytics and data management, he warned (sensibly in my view) that we should not think that the solution to future crises is simply to set up a massive data collection and Modelling entity (see earlier post on the proposed ECB data utility)

Clifford thinks that Dodd-Frank has the potential to do for the CRO role what Sarbanes-Oxley did in elevating the CFO role. He wants risk managers to take the opportunity presented in this post-crisis period to lead the way in promoting good judgement based on sound management of data and Analytics. He warned that senior management buy-in to risk management was essential and could be forced through by regulatory edict.

This last and closing point is where I think where the role of risk management (as opposed to risk reporting) faces it's biggest challenge, in that how can a risk manager be supported in preventing a senior business manager from seeking a overly risky new business opportunity based on what "might" happen in the future - we human beings don't think about uncertainty very clearly and the lack of a resulting negative outcome will be seen by many to invalidate the concerns put forward before a decision was made. Risk management will become known as the "business prevention" department and not regarded as the key role it should be.

Posted by Brian Sentance | 24 June 2011 | 3:26 pm

SIFMA declines...

I almost forgot to mention that I went along to the SIFMA event (previously known as the SIA show) last week to take a look around. For those of you not familiar with the SIFMA/SIA event, then it was the biggest financial services technology event I had ever attended/exhibited, taking up 5 massive floors at the Hilton NY. Everyone used to go there, and indeed that was one of the reasons (the only reason?) to go along. Now the event seems to dying a slow death, something I was going to write about but Adam Honore of Aite and Melanie Rodier beat me to it.

Posted by Brian Sentance | 23 June 2011 | 5:45 pm

PRMIA on Systemic Risk Part #2 - plus the OFR

Lewis Alexander (ex-US Treasury) carried on the theme of systemic risk at the PRMIA seminar "Risk, Regulation and Financial Technology & Identifying the Next Crisis". He started by saying that whilst systemic risk was a risk to the economy and industry as a whole, systemic risk was also relevant to the risks (such as market or credit) that a risk manager at an individual institution needs to assess.

Lewis said that there had really only been three systemic crises over the past century or so (1907, 1933 and 2008) with obviously many more disruptions in markets that should not be described at systemic. As such this is one problem of assessing systemic risk which is that crises are rare events so there is little data to analyse. He also warned that the way the system responds to small shocks should not be taken as a proxy for how it responds to large ones, that the relationship between asset prices and systemic risk is a complex one, and that reporting (mainly accounting but also in risk) had not kept up with financial markets innovation.

Lewis said that "stress test" methods can help to identify vunerable institutions but that this method of looking at systemic risk does not deal with the propogation of risk from one institution to another. He said that network analysis can help to assess propogation but the weakness with these methods was the lack of counterparty data. Liquidity methods also suffer from a lack of data. He said that "Leading Indicators" (see past post on Bubble Indices) tell us little of what creates systemic risk.

He mentioned the use of CoVaR (based on VaR) for systemic risk, using CDS pricing to theoretically "insure" the industry against crisis and a "Merton Model" approach to estimate potential losses due to default for a group of banks. He said that all of these models were good comparators, but not good as indicators.

Given the previous talk on systemic risk, Lewis switched his focus to what can done with the main focus for him being data where we need:

Robust data on both asset and counterparty exposures
Data on leverage through the system
Data on the depth of liquidity to assess the vunerability of assets to fire sales

A final few points from his talk:

Dodd-Frank will help given new reporting mandates e.g. swap data repositories being invaluable sources of data for regulators
Could we use the payments/settlement system to provide yet more insight into what is going on by sensibly tagging transactional flows (DTCC take note apparently!)
SEC registration of a new financial product could help to enforce what is reported, how and to act as a limit on what products can be sold
Lewis said that up to 5,000 attributes are needed to describe any financial transaction so it can be done
As he became involved in the FSOC and the formation of the OFR he thought initially that collecting all the data needed was impossible, but his view has changed on this with modern technology and processing power.
The above said, he thought that until standards were in place (such as LEI) then it did not make sense for the OFR to start collecting data
A member of the audience suggested that if data could be published in a standard form, it would "Google to the rescue" in terms of doing aggregation across the industry without centralising the data in one store. (maybe Google plans to usurp Microsoft Excel as the defacto trading and risk management system for the industry?)

Lewis gave a very good and interesting talk. I think some of his ideas on the OFR were good, but given the state of the data infrastructure that I have observed at many large institutions I would be worried that he is being optimistic on how quickly the industry is able to pull all the data together, however standardised. I think the industry will get there (particularly if mandated), but given the legacy of past systems and infrastructure it will take some good time to achieve yet.

Posted by Brian Sentance | 22 June 2011 | 8:59 pm

PRMIA on Systemic Risk Part #1

I attend a PRMIA seminar this morning at the offices of Ernst & Young with the rather long title of "Risk, Regulation and Financial Technology & Identifying the Next Crisis".

First up was Matthew Richardson of NYU Stern with a presentation entitled "Identifying the Next Crisis". The focus of his presentation was on systemic risk, which he defined as the risk that financial institutions lose the ability to intermediate (i.e. continue to provide services) due to an aggregate capital shortfall. He presented a precise definition of the systemic risk of a firm as:

Expected real social costs in a crisis per dollar of capital shortage
x Expected capital shortfall of the firm in a crisis

Matthew explained that there are three approaches to estimating systemic risk contribution:

Statistical approach based on public data
Stress tests
Market approach based on insurance against capital losses in a crisis

He explained that the methods his team have used have had some statistical success against data from the past crisis in showing those organisations in crisis early. I found his presentation reasonably dry (more regression analysis etc) but I thought the following where worth a mention:

Crisis Insurance - Approach 3 on getting firms to insure themselves against capital shortfalls in a crisis sounded interesting but ended up with the insurer being the regulator (not enough capital to insure privately) and the beneficiary being the regulator. So effectively this was a tax on the systemically significant institutions, where the involvement of the private insurers was mainly to do with price discovery (i.e. setting the right level of premium (i.e. tax) for each institution)
Short-term Indicators - Many of the approaches we have currently (VaR etc) are short term indicators and so in good times do not inhibit market behaviour as would be desired by the regulators. A good illustration was given of how short term volatility was very much lower than long term prior to the crisis and how these merged to similar levels once the crisis hit.
Regulatory Loopholes - He put forward that this crisis was as a result not of monetary policy but of large complex financial institutions exploiting loopholes in regulation. The AIG Quarterly Filings of Feb 2008 showed that $379Billion of the $527Billion of CDS were with clients that were explicitly seeking regulatory capital relief (i.e. get the CDS in place and your capital requirement dropped to zero). He also explained how Fannie Mae and Freddie Mac were used by banks to simply "rubber stamp" mortgage pools and magically reduce the capital required down from 4% to 1.6%.
Where to look - He said that "like water flows downhill, capital flows to its most levered point". He said to look for which parts of the financial sector are treated different under Dodd-Frank, Basel III etc and that the key candidates were 1) shadow banking and 2) government guarantees. Also you should look for those asset classes that get preferred risk weights for a given level of risk.

As often seems to be the case, I found the side comments more interesting than the main body of the presentation, but Matthew's presentation showed that a lot of work is being done on systemic risk identification and measurement in academia.

Posted by Brian Sentance | 22 June 2011 | 8:53 pm

A glass of red and contrary ideas on Triple-A risk

I enjoyed myself at the drinks reception after the NYU-Poly event. Nothing new in that I guess for those of you that know me well and like me find it difficult to resist a glass or two of red wine. Whilst attempting to circulate (I am almost 2 metres tall, so rather than "circulate" I think a more appropriate word might unfortunately be "intimidate"), I struck up a conversation with an interesting gentleman by the name of Per Kurowski.

Per is a former director of the World Bank and has some contrary and interesting ideas on the financial crisis and our current methods of regulation. His first that financial crises rarely start with assets that are perceived as "risky", which I think is a pretty self-evident point but not one that I had not previously registered. His second line of argument is that our current regulation biasses our banks away from "riskier" assets and hence away from just the kinds of organisations that are a) needed for employment creation and b) do not cause crises.

Per argues that many of the big institutions are near triple-A rated and hence benefit from being able to leverage up cheaply (at low-interest rates, since they are triple-A) and are then biased by lower capital requirements to use this leveraged funding to invest in yet more triple-A assets (SPVs/other institutions such as themselves). Hence you get the double-whammy of cheap funding and biased capital requirements which naturally leads to potential distortions in anything perceived as triple-A, and a bias away from riskier assets and the risk-takers that the world economy needs.

Per expands upon these arguments in his blog and on YouTube.

Posted by Brian Sentance | 20 June 2011 | 10:13 pm

Removing the punchbowl at NYU-Poly

A few of choice quotes from the rest of the day at NYU-Poly:

"The difference between economists and meteorologists is that meteorologists can at least agree on what happended yesterday"
"A bubble can only be identified from a trend when the bubble bursts"
"Capital flows from strange places to strange destinations in today's financial markets"
"In a Basel III world, the stock price of Morgan Stanley would rise if its investment banking division were sold off"
"Basel III is a good attempt at managing systemic risk"
"Hedge Funds are the risk takers of the future"
"Hedge Funds have the partnership mentality that the commercial banks have lost and should regain"
"CCPs should not compete on risk management"
"Economists are trained to predict everything except the future"
"Dodd Frank was a missed opportunity to consolidate the many regulators in the United States"
"Washing D.C. is all about turf and theatre"
"Insolvency and liquidity risk are not clearly separable"
"Beware the Golden Rule. He who makes the Gold makes the Rule"
"Systemic risk is not the sum of individual institutional risk"
"As Chuck Prince said "As long as the music is playing, you’ve got to get up and dance""
"Systemic risk management only works when we all stop dancing"
"Regulation should remove the punchbowl just when the party is getting started"

Posted by Brian Sentance | 20 June 2011 | 8:43 pm

Regulation - Putting out the fire once you know where the fire is - NYU-Poly

The first panel session at NYU-Poly after Nassim Taleb concerned itself with the increasing competition between banks and insurers, which I didn't think reached any great conclusions as to where things are heading but did give background for why banks and insurers are increasingly offering the same services (disintermediation, regulation and industry structural changes being the main reasons). One of the presenters also said that acturial methods may provide a useful framework for unhedgeable risks taken by banks. I must acknowledge that my attention span was also challenged during this session by a very early start (up pre-6am) and a distinct lack of caffeine (later rectified many times over).

Second panel session up was entitled "The Future of Financial Regulation" and proved a lot more interesting to me given that I think I learned a few new things. Main presenter was Allen Ferell from Harvard Law School. Main point I took away from this presentation was that regulation should focus more on the resolution of financial distress after (ex-post) it has occurred at an institution rather than rules and regulations to prevent it before it happens.

I found this argument quite appealing since to a large degree it avoids provisioning for the "unknown unknowns" through more and more rules and increases in capital. The reduction in pre (ex-ante) rules would also reduce the gaming of the rules that enevitability would occur, and shareholders knowing that they would be penalised and penalised quickly following financial distress would encourage them to become more interested in the levels of risk being taken on their behalf. I guess one of the main issues for the above is how such a level of financial distress would be defined and enforced in order to act as a trigger for say automatic conversion of debt to equity. Anyway, on with what Allen Ferrell had to say:

Allen said that if a financial institution had had the foresight to see the financial crisis coming, then looking across the industry there would have been a great variation in the amount of capital needed to survive the crisis. I guess here the implication here was that higher levels of capital across the industry will help, but they are unlikely to be enough for some organisations in the crisis to come.

After the crisis had hit, he said that financing from the repo market dried up as repo haircuts exploded, and he said that this was like the modern day equivalent of a bank run (where a solvent bank faced difficulty due to having to sell good assets cheaply to satisfy demands for returning of cash deposits).

Allen said that leverage and "debt overhang" made it much less likely that a financial institution would get in more equity capital following the crisis since it implied a transfer of wealth from the stockholders to bondholders. More of this important point later.

He put forward that it was not yet clear whether the 2007-8 crisis was mainly due to insolvency or due to a bank run. He argued that it was some combination of both, and referred back to the recent re-assessment of the Great Depression being caused not by a run on (solvent) banks but rather by flight of retail investors away from insolvent banks.

He concluded that much of the action for any future crisis will have to take place after any new crisis hits (ex-post), partly due to his assessment of the disconnect between equity capital needed (the current focus of things like Basel III) prior to a crisis and an institution's financial health following a crisis.

Allen suggested that contingent capital, i.e. debt capital that automatically converted in equity based on some market trigger might be very helpful in dealing with a financial crisis. Such a conversion would happen early than if an institution agreed to it earlier and would automatically dilute existing stockholders. Overall this was a thought provoking talk and the panel discussion afterwards was interesting too. One of the panelists commented that he looked for a high leverage and high ratios of CEO to CRO compensation as his measure of where to look for the next set of risky institutions. The panel also seemed to agree that with the benefit of hindsight, allowing Lehmans to fail and the resultant drying up of the money markets was a mistake, and more consistency was needed in bankruptcy and distress resolution.

Posted by Brian Sentance | 18 June 2011 | 4:23 pm

Taleb and Model Fragility - NYU-Poly

I went along to spend a day in Brooklyn yesterday at NYU-Poly, now the engineering school of NYU containing the Department of Finance and Risk Engineering. The event was called the "The Post Crisis World of Finance" was sponsored by Capco.

First up was Nassim Taleb (he of Black Swan fame). His presentation was entitled "A Simple Heuristic to Assess Tail Exposure and Model Error". First time I had seen Nassim talk and like many of us he was an interesting mix of seeming nervousness and confidence whilst presenting. He started by saying that given the success and apparent accessibility to the public of his Black Swan book, he had a deficit to make up in unreadability in this presentation and his future books.

Nassim recommenced his on-going battle with proponents of Value at Risk (see earlier posts on VaR) and economists in general. He said that economics continues to be marred by the lack of any stochastic component within the models that most economists use and develop. He restated his view that economists change the world to fit their choice of model, rather than the other way round. He mentioned "The Bed of Procrustes" from Greek mythology in which a man who made his visitors fit his bed to perfection by either stretching them or cutting their limbs (good analogy but also good plug for his latest book too I guess)

He categorized the most common errors in economic models as follows:

Linear risks/errors - these were rare but show themselves early in testing
Missing variables - rare and usually gave rise to small effects (as an aside he mentioned that good models should not have too many variables)
Missing 2nd order effects - very common, harder to detect and potentially very harmful

He gave a few real-life examples of 3 above such as a 10% increase in traffic on the roads could result in doubling journey times whilst a 10% reduction would deliver very little benefit. He targeted Heathrow airport in London, saying that landing there was an exercise in understanding a convex function in which you never arrive 2 hours early, but arriving 2 hours later than scheduled was relatively common.

He described the effects of convexity firstly in "English" (his words):

"Don't try to cross a river that is on average 4ft deep"

and secondly in "French" (again his words - maybe a dig at Anglo-Saxon mathematical comprehension or in praise of French mathematics/mathematicians? Probably both?):

"A convex function of an average is not the average of a convex function"

Nassim then progressed to show the fragility of VaR models and their sensitivity to estimates of volatility. He showed that a 10% estimate error in volatility could produce a massive error in VaR level calculated. His arguments here on model fragility reflected a lot of what he had proposed a while back on the conversion of debt to equity in order to reduce the fragility of the world's economy (see post).

His heuristic measure mentioned in the title was then described which is to peturb some input variable such as volatility by say 15%, 20% and 25%. If the 20% result is much worse than the average of the 15 and 25 ones then you have a fragile system and should be very wary of the results and conclusions you draw from your model. He acknowledged that this was only a heuristic but said that with complex systems/models a simple heuristic like this was both pragmatic and insightful. Overall he gave a very entertaining talk with something of practical value at the end.

Posted by Brian Sentance | 17 June 2011 | 6:14 pm

More formal management of instrument valuation needed

Xenomorph has today released its white paper “Instrument Valuation Management: management of derivative and fixed income valuations in a multi-asset, multi-model, multi-datasource and multi-timeframe environment”.

The white paper expands on the “Rates, Curves and Surfaces – Golden Copy Management of Complex Datasets” white paper Xenomorph published recently (see earlier post) and describes how, despite the increasing importance of instrument valuation to investment, trading and risk management decisions, valuation management is not yet formally and fully addressed within data management strategies and remains a big concern for financial institutions.

Too often, says Xenomorph, valuations (and the analytics used to process input and calculate output data) fall between traditional data management providers and pricing model vendors. This leads to the over–use of tactical desktop spreadsheets where data “escapes” the control of the data management system, leading to an increased operational risk.

Whilst instrument valuation is certainly not the primary cause of the recent financial crisis, the lack of high quality, transparent valuations of many complex securities resulted in market uncertainty and in the failure of many risk models fed by untrustworthy valuations.

“A deeper understanding of financial products reduces operational risk and promotes quality, consistency and auditability, ensuring regulatory compliance”, says Brian Sentance, CEO Xenomorph. “Clients’ requirements have evolved and portfolio managers, traders and risk managers recognize that it is no longer sufficient to treat valuation as an external, black-box process offered by pricing service providers”, he adds.

Nowadays, regulators, auditors, clients and investors demand even more drill-down to the underlying details of an instrument’s valuation. It is therefore important to implement an integrated, consistent analytics and data management strategy which cuts across different departments and glues together reference and market data, pricing and analytics models, for transparent, high quality, independent valuation management.

“Our TimeScape solution provides a valuation environment which offers rapid and timely support for even the most complex instruments, allowing our clients to check easily the external valuation numbers, based on their choice of model and data providers”, says Sentance. “Otherwise, what is the point of good data management if the valuations and the analytics used are not based on the same data management infrastructure principles?”

For those who are interested, the white paper is available here.

Posted by Sara Verri | 4 May 2011 | 12:41 pm

Investment risk not rewarded

Interesting article from the FT, Reward for risk seems to be a chimera, effectively saying that more risky (volatile) equities do not necessarily provide higher returns than less risky equities. I like the suggestion that the reason for this is that "hope springs eternal" and investors buy more volatile stocks (pushing up price) in the hope of higher returns. However, as yet another illustration of the law of unintended consequences, the article goes on to suggest that choosing a benchmark index to outperform and limitations on borrowing imposed by investment mandates may both be driving this effect, are interesting and challenging ideas for investment managers.

Posted by Brian Sentance | 31 March 2011 | 7:14 pm

Rates, curves and derived data management remains a neglected area following the crisis

Xenomorph has released its white paper 'Rates, Curves and Surfaces – Golden Copy Management of Complex Datasets'. The white paper describes how, despite the increasing interest in risk management and tighter regulations following the crisis, the management of complex datasets – such as prices, rates, curves and surfaces - remains an underrated issue in the industry. One that can undermine the effectiveness of an enterprise-wide data management strategy.

In the wake of the crisis, siloed data management, poor data quality, lack of audit trail and transparency have become some of the most talked about topics in financial markets. People have started looking at new approaches to tackle the data quality issue that found many companies unprepared after Lehman Brothers' collapse. Regulators – both nationally and internationally – strive hard to dictate parameters and guidelines.

In light of this, there seems to be a general consensus on the need for financial institutions to implement data management projects that are able to integrate both market and reference data. However, whilst having a good data management strategy in place is vital, the industry also needs to recognize the importance of model and derived data management.

Rates, curves and derived data management is too often a neglected function within financial institutions. What is the point of having an excellent data management infrastructure for reference and market data if ultimately instrument valuations and risk reports are run off spreadsheets using ad-hoc sources of data?

In this evolving environment, financial institutions are becoming aware of the implications of a poor risk management strategy but are still finding it difficult to overcome the political resistance across departments to implementing centralised standard datasets for valuations and risk.

The principles of data quality, consistency and auditability found in traditional data management functions need to be applied to the management of model and derived data too. If financial institutions do not address this issue, how will they be able to deal with the ever-increasing requests from regulators, auditors and clients to explain how a value or risk report was arrived at?

For those who are interested, the white paper is available here.

Posted by Sara Verri | 24 February 2011 | 5:45 pm

2010 Risk in Review NY

I went along to a a Prmia event last night "2010 - Risk Year in Review". The event started with a somewhat overwhelming brain dump of economic and credit statistics from John Lonski, Chief Capital Markets Economist at Moody's Analytics. In summary he seems very bullish about corporate credit spreads tightening given the way in which corporate profit growth is surging ahead of debt growth. His main concern for the economy was maybe unsurprisingly the US housing market and whether this will bottom out and start to rise in 2011. Given fiscal imbalances and competition from emerging markets he did not think that inflation was a big risk despite activity such as QE2.

Robert Iommazzo of search firm Seba International did a fairly dry presentation on industry compensation for risk managers. Seba seem to getting around having had a big presence at Riskminds in Geneva last week. This section only livened up when the questions started after the presentation, and is probably worth noting that the UK FSA is being perceived as a "Big Brother" with its involvement in setting compensation policies in financial markets. Obviously the FSA is not heading back to the heady days of the 1970's where central government set industry pay rises (journalists please note this meant you back then!), but it is also obvious that such control over an individual's remuneration is something that goes totally contrary to an American way of thinking. UK Government needs to be mindful of this perception particularly if it leaves itself open to arbitrage on compensation policy from other financial centres.

Panel debate followed, involving Ashish Das of Moody's, Yury Dubrovsky of Lazard Asset Management, Jan H. Voigts of the NY Fed and Christopher Whalen of Institutional Risk Analytics. Main points:

Chris said that he was one who was predicting a further fall in the housing market next year, and he asked the audience that when they looked at economic statistics, credit spreads,the Vix, bond spreads, did anyone getting the feeling the things are "normal" yet? Using these numbers and plugging them into a model does any believe the results are stable and can be relied upon? The audience fundamentally seemed to agree with these "warning" questions.
Jan asked the audience to consider how believable is your data and to try to understand what data is critical for your business and that is imperative to create tools to manage this data appropriately. Jan said that the biggest challenge for financial institutions going forward is how to calibrate what rate/volume/type of business you can transact safely and that this needed a lot more consideration.
Yury said that he finds that the risks present in 2008 are still around in 2010, but now with the addition of European sovereign credit problems and the raft of regulation heading towards the industry. To add to this pessimistic note, he also said that some of the interest in "hot" emerging markets such as the BRICs was resulting in investments in lower quality IPOs relative to previous years.
Ashish thought that systemic risk was going to become more important for the industry. With the setting up of the Office of Financial Research (OFR) next year, he suggested that the industry needed to take much more of a lead in sorting out its own house in advance of letting the regulators do so. On the subject of models, he said that models should supplement human judgement but not replace it, and mentioned the quote by George E. P. Box that "all models are wrong, but some are useful".
Chris suggested that the role of risk managers will become more like that of a credit collector, with more involvement in actually seeing what can be recovered once a default has occurred. He also suggested that the industry should create its own consensus-based ratings (supplemented by the existing CRAs) to get a more reliable view of credit.
Ashish echoed some of the speakers last week at Riskminds in saying that regulatory compliance is not risk management, and that practitioners should do more to guide the regulators.
On the subject of risk culture, Yury asked how many risk managers knew data, quant, markets and how to deal with the egos of traders and senior management. This last point seemed to be conceded by the audience as a major weakness of the risk management profession and goes back to whether a risk manager is willing to put his career on the line to go against accepted business strategy.
Chris added that having worked at several investment banks he had not yet experienced a risk manager attending a senior committee, let alone a risk manager speaking up against a senior trader. He talked of two business models "Paranoid and Nimble" and "Well Documented and Pedantic" with the second one being the only one possible in his view once a business gets to a certain size.
On the subject of Government Sponsored Enterprises (GSEs like Fannie Mae and Freddie Mac) Chris said that the role of these will be up for review by the end of 2011. He thinks that the banks will head back towards actually holding mortgages and loans and the GSEs will become more conduits rather than direct sources of finance. This was news to me, given that so far the GSEs have been notably left out of recent reviews of what went wrong with the recent crisis.

Panel was very good, all speakers very knowledgeable. "Regulation is not risk", "models are not perfect", "risk governance" and "take control of your data" were all themes that echoed last week's RiskMinds event, allbeit with more of an American rather than international viewpoint on the economy, regulation and markets.

Posted by Brian Sentance | 15 December 2010 | 5:16 pm

RiskMinds 2010 - Day 3 - Financial Engineering Blueprint

Panel moderated by Ricardo Rebanato of RBS on "Determining The New Blueprint For Financial Engineering". It seems like Ricardo has been busy following up on his talk from last year (see post) with the release of his book on scenarios (no I am not on commission for this but thought it may be interesting to take a look at!).

Summary of main points from the panel debate:

Regulators would like simpler models but simpler does not mean better, complex models do not mean worse.
It is the thoughtful application of a model that is important, not the level of complexity in itself.
Given the more complex world we live in, more complexity in modelling is both needed and desirable if things are to improve in risk.
Some members of the panel thought that regulation had stifled innovation in risk models (as opposed to valuation models) through insisting on conformity of reporting. The innovation is limited since the regulators simply set the rules and then the game begins of the bank optimising against these rules.
Evan Picoult of Citi disagreed with this, saying that his own group now look at historical events going back over 100 years for possible scenarios as opposed to the last few years (comment: interesting to see someone using more history as a complement to more forward-looking risk modelling)
Riccardo asked whether there is a conflict between what a regulator wants (lack of risk) and what a rational CEO wants - should a CEO for example accept a level of risk of disaster for the bank of 1 in 50. Evan argued that the banks should be more transparent to allow investors in bank stock and bonds to decide and price-in the policies implemented by bank management.
Only around 15% of the audience thought that greater pricing/valuation model validation would have changed the 2007-2009 crisis. John Hull said that he had received many emails trying to apportion blame in this way which he rejected. Concensus seemed to be the route cause was the lack of common sense over the mortgage market.

Good debate with Riccardo doing more than just moderating, but not a great deal new relative to recent years. In summary my feeling for RiskMinds 2010 was high quality speakers but a little subdued from the embarassment of 2008 and the anger against the regulators in 2009. Maybe we should all want more subdued risk management conferences but it will be interesting to see what 2011 brings and whether energy levels are up.

Posted by Brian Sentance | 9 December 2010 | 3:19 pm

RiskMinds 2010 - Day 2 - Hugo Banziger - New Risk Management Agenda

Hugo Banziger of Deutsche Bank gave a presentation entitled "Reshaping The New Agenda for Risk Management".

Hugo started by saying by outlining the ways in which regulation is changing the markets. Whilst positive overall on the benefits of regulation, he expressed surprise at the regulation on OTC derivatives which he say as helpful in managing risk, and not the cause of the crisis.

He emphasised that whilst regulation is important, that regulation should not be a substitute for risk management and said that regulation in particular does not address:

The quality of assets held
The quality of management
The quality of infrastructure

He additionally mentioned that whilst welcoming Basel III, the economic effect will be to make the supply of credit more expensive to the detriment of economic growth in the real economy.

Given the quality issues he identified above, he then moved on to show what he had done about them in terms of:

People - what quality of people do you have?
Processes - where are the holes that things will fall through?
Systems - can you get a complete picture of risk?
Portfolio Level Risk - across all asset classes and business units

On people, he advocated a "home grown" risk management team, with people rotated across different roles within risk. He takes the fact that other institutions hire his staff as a frustrating complement to Deutsche Bank risk management. He has implemented a "passport" for his staff which shows what they are trained/competent in and how they are annually tested against this, across both technical and softer management skills. He was funny and quite dismissive that if "anyone does not know what an option is and how it works they are out!" even for lawyers as well as risk managers.

On processes, he has set up a new "risk operations" centre of competence to centralise form filling for risk managers, enabling risk managers to spend more time on risk and less on admin. He stated flatly that just because you find that risk managers spend 50% of their time on admin does not mean you have to accept this and you can do something about it. He also said that he is moving the jobs to where the people are, rather than asking people to move (e.g. risk centre in Berlin to catch Berlin maths graduates).

On systems he has spent EUR30 million in 20 months on sorting out the consistency of data and models within market risk. During the crisis it took DB 48 hours to pull together their mortgage portfolio exposure which was too long. He says that initiatives like this are part of a 10 year investment in systems, data and analytics. His ultimate aim is to have an interactive real-time control centre for all risks in the bank and to move away from paper-based daily reporting. He also mentioned that he had grown his market risk team from 70 to 200 post-crisis.

On Portfolio Risk he says that more time needs to spent on knowing risk apetite and knowing how this fits against risk capacity for the bank. He emphasised that risk managers are there to defend P&L and not capital. He said that portfolio/business model risks were his biggest source of risk.

Inspiring speaker, very confident, open about past losses and mistakes made. Biggest difference to many speakers here was that he put forward tangible actions to address things such as risk culture rather than just talking around them.

Posted by Brian Sentance | 8 December 2010 | 10:00 pm

RiskMinds 2010 - Day 2 - Paul Embrechts on Financial Engineering

I started the day with a presentation by Paul Embrechts (see previous RiskMinds post here) entitled "Financial Engineering And The Financial Crisis: Warnings, Guilt and Lessons Hopefully Learned".

Paul first pointed out that if ever there was a bubble, it was a "bubble" of books on the crisis and its causes. He listed a number of reasons for the crisis, most interesting/new of which was in addition to "too big to fail", was "too big to save" as a new risk given the size of some financial institutions relative to the economies they operate in. Paul has an extensive academic background in both financial risk management and actuarial studies, and I guess it was with his actuary hat on he said that the three main problems for financial engineers going forward were "social insurance, social insurance and social insurance".

By social insurance Paul was referring to medical, life and health insurance and saw this as his big concern for the future. He illustrated this through showing the age distribution of the Japanese population from 1950, 2007 and 2050. Basically the 1950 distribution was like a pyramid with a very young population underneath the middle and old-aged. This shape changed to being fatter in middle age for 2007, and is predicted to be inverted with more older people that younger in 2050. Given that this will result in a percentage reduction of more than 10% in working population supporting an increasingly older population it was not difficult to see what he was meaning.

Paul spent some time going through old papers from him and others (particularly Joseph Stiglitz on securitisation in 1992) warning of the 2008 crisis - I do not know his work well enough to know how much this was "wise after the fact" but what he mentioned on securitisation and correlation made sense given what has since happened. Worth taking a look on his website for some of his papers on Basel and risk management I guess.

One key point he made was simply about volume. Working admittedly on a notional basis, he said that having an OTC derivatives market with notional value of $583 trillion is interesting in the context of a world economy with GDP of only $58 trillion. Even netting notional down you get around $30 trillion of OTC derivatives which still deserves our attention and our efforts to make things better in risk management. I guess his simple message here was "pay attention to volume".

He said the use and abuse of the Repo 105 rule was worth looking at (so I will, anyone with knowledge please let me know), and also questioned the societal benefit of high frequency trading (HFT). Looking back at the Flash Crash Paul said that this was a new kind of risk for risk managers and he had no idea how to hedge it.

Paul defended mathematics as the solution to some of the problems of the crisis and not as the cause - fundamentally he thinks the press have given maths bad PR. He said that we should all watch out for the word "new" being used, as this indicates the start of a bubble with phrases such as the "new economy". Overall a great speaker very comfortable with his subject matter.

Posted by Brian Sentance | 8 December 2010 | 9:28 pm

RiskMinds 2010 - Day 1 - Risk Governance

I am over in Geneva at the moment (taking a break from the harsh English winter?..) for the RiskMinds 2010 event. Despite its slightly pretentious title (I leave it to you to assess how appropriate the name seemed in 2008...) it is one of the best attended risk management events where risk managers discuss what is going on and what is new to risk management. You can find some posts from the 2009 event here, and the 2008 event here - both make interesting reading given that we are out of the crisis now (aren't we?).

I arrived late for the first day, just to catch a panelist Pippa Malmgren of the Canonbury Group saying that during the crisis everyone knew they were long highly leveraged, very risky assets that were potentially in a pricing "bubble" but when asked about whether this bubble, most used one of the three responses:

Asset managers said it didn't matter so long as all of our peers go down too...
Hedge fund managers said that there business was to surf market waves and they could restart the fund afterwards anyway...
I know it's a bubble but I will be able to get out before it bursts...

Pippa added that the last was the most worrying response, although I guess all are still relevant negative insights into the attitudes of some financial market participants.

The next panel was on Risk Culture & Ethics with Richard Evans of Citi first up presenting on issues resulting from the crisis. Richard suggested that the following key issues were missed during the crisis:

Silo Mentality - Risk reports were not comprehensive enough, covering all assets, regions and business units in one; risk management focussed too much on validating individual deal flow (transactions) rather than the portfolio; there were no incents for business managers to share resources and information.
Short Term Revenue Focus - Focus was on short-term bonuses were not related to profitability after costs and cost of risk capital were taken into account.
Backward-Looking Models - Models looked backwards (historic VAR for instance) rather than being forward-looking, scenario-based. Richard said that Citi now combine both backward-looking VAR and multiple (severe) scenarios on an approximately 50-50 basis when assessing overall risk now.
Poor Teamwork - Trading and risk management staff did not work together effectively during the crisis. Richard now suggests this must be addressed through greater involvement of the business in risk management, the introduction of the risk committee and fighting against risk management "ivory towers".
Board Weakness - Richard said that boards and senior management committees were not set up to react to "alarm bells" such as triggers resulting from limit breaches; Also many boards were simply very weak in their basic understanding of the risks being taken by the business.

I don't think the above will come as any surprise to anyone who has followed the crisis but Richard is a good speaker and so his presentation was entertaining. He later went on to criticise regulators for asking him to replace staff members with 20 years experience with others with 20 years experience - he said that he had not yet found a way to cram 10 years experience in 2 years although maybe recent times have come close to this aim! He also said that firms where the "mood" of the CRO affected what approval decisions were made obviously did not have strong enough governance in place. Richard wants risk and trading staff to work closer together, although he admits that two years on it is difficult to get business level compensation to get traders to work in risk - in this regard he also mentioned his days at JPMorgan when trading and risk staff spent time seconded to the regulators for a time.

There were a variety of other speakers during the day, all dealing with risk governance and culture. Whilst vital to the changes that must be made in the culture of the majority of institutions, I think it is a difficult topic to talk about, since it is hard to express just what needs to be "done" in some pragmatic way. Put another way, the conversations on this topic tend to focus on the need for a risk management culture and become very wooly when discussing how one is implemented. A presentation by Alden Toevs of the Commonwealth Bank Australia attracted discussion by some of the attendees over coffee. The presentation was about how to formalise/make a process of the discussion and agreement of risk appetite (a Risk Appetite Statement) between board, risk management and the business. Alden suggested that the use of anonymous "voting" technology at board level encouraged more openness and discussion, and getting the business involved in this process was a great way to encourage the involvement of trading in risk management. A good presentation in both content and effects (an iMac user I think!) and an amusing speaker who pointed out that visiting the Australian parliament is interesting for a risk manager given that you are surrounding by genuine Black Swans in the lake outside...

Posted by Brian Sentance | 8 December 2010 | 8:36 pm

The current bad luck of the Irish

If like me you are puzzled as to:

Why the Irish need a financing package now when they don't need to borrow for at least another 6 months?
Why adding more debt on top of bad debt makes things better?
Why bondholders of failed banks don't get forceably converted to holding equity and original equity holders get nothing?

Then take a read of the this article from the FT. We live in interesting economic times.

Posted by Brian Sentance | 23 November 2010 | 4:20 pm

Risk USA - 15 cents in the dollar isn't good...

I went along to the Risk USA event yesterday and caught a good panel in the afternoon called “Garbage in, garbage out” Servicing the data supply and analytic needs for risk management.

In particular, one of the speakers, Frank R. Brown, described some work he had done as a consultant at one financial institution on tracking and rebalancing an index product. To do this, Frank had to integrate the constituent instrument symbology of the:

Custodian
Index Provider
Real-Time Data Provider
Rebalancing Software
In-house Trading System

On top of this, corporate events might result in changes to symbology that not all providers would be up to date on, with various lags before all had caught up with the corporate action (rebalancing software often late, custodian often not changing symbol at all). He mentioned that he did all of this symbology management manually in Excel.

Of his time, he said he spent:

65% on managing the symbology and dealing with data issues
20% managing the various vendor APIs in Excel to update the data
15% on tracking and rebalancing

To sum up, he said that a productive work level of 15 cents in the dollar wasn't good value for the client and yet the issue continues on and on. I don't think that his example was particularly earth shattering in terms of newness, but it put in a very simple and pragmatic context the importance of doing some of the simple things right and the benefits of a more automated approach to data management, even before you delve into the data quality/validity issues of the market data itself.

Just to end on an entertaining note, then back to the title of the talk on "Garbage-in, garbage-out..." the panel moderator (Domenic Iannaccone of Sybase) put forward a good quote he had heard:

"If everyone used the same garbage at least that would be a step forward!"

Transparency and consistency can take many forms, but I didn't know it needed to apply to incorrect data too!...

Posted by Brian Sentance | 4 November 2010 | 7:15 pm

A French Slant on Valuation

Last Thursday, I went along to an event organized by the Club Finance Innovation on the topic of “Independent valuations for the buy-side: expectations, challenges and solutions”.

The event was held at the Palais Brongniart in Paris, which, for those who don’t know (like me till Thursday), was built in the years 1807-1826 by the architect Brongniart by order of Napoleone Bonaparte, who wanted the building to permanently host the Paris stock exchange.

Speakers at the roundtable were:

Eric Benhamou, CEO Pricing Partners
Francis Cornut, Président DeriveXperts
Jean-Marc Eber, Président LexiFi
Patrick Hénaff, Associated Professor at the University of Bretagne (see model validation paper for additional background)
Claude Martini, CEO Zeliade Systems

The event focussed on the role of the buy-side in financial markets, looking in particular at the concept of independent valuations and how this has taken an important role after the financial downturn. However, all the speakers agreed that remains a large gap between the sell-side and buy-side in terms of competences and expertise in the field of independent valuations. The buy-side lacks the systems for a better understanding of financial products and should align itself to the best practices of the sell-side and bigger hedge funds.

The roundtable was started by Francis Cornut of DeriveXperts, who gave the audience a definition of independent valuation. Whilst valuation could be defined as the “set of data and models used to explain the result of a valuation”, Cornut highlighted how the difficulty is in saying what independent means; there is in fact a general confusion on what this concept represents: internal confusion, for example between the front office and risk control department of an institution, but also external confusion, when valuations are done by third-parties.

Cornut provided three criteria that an independent valuation should respect:

Autonomy, which should be both technical and financial;
Credibility and transparency;
Ethics, i.e.: being able to resist to market/commercial pressure and deliver a valuation which is free from external influences/opinions.

Independent valuations are the way forward for a better understanding of complex, structured financial products. Cornut advocated the need for financial parties (clients, regulators, users and providers) to invest more and understand the importance of independent valuations, which will ultimately improve risk management.

Jean-Marc Eber, President LexiFi, agreed that the ultimate objective of independent valuations is to allow financial institutions to better understand the market. To accomplish this, Eber pointed to the fact that when we speak about services to clients, we should first think of what are their real needs. The bigger umbrella of “buy-side” implies in fact different needs and there is often a contradiction on what regulators want: on one side, having independent valuations provided by independent third parties; on the other side, independent valuations really mean that internal users/staff do understand what there is underline the products that a company have.In the same way, we don’t just need to value products but also measure their risk and periodically re-value them.It is important, in fact, to have the whole picture of the product being evaluated in order to make the buy-side more competitive.

Another point on which the speakers agreed is traceability: as Eber said, financial products don’t exist just as they are, but they go under transformation and change several times. Therefore, the market needs to follow the products across its life cycle till its maturity stage and this pose a technology challenge, in providing scenario analysis for compliance and keeping track of the audit trail.

At the question, ‘what has the crisis changed’ panellists answered:

Eber: the crisis showed the need to be more competent and technical to avoid risk. He highlighted the need to understand the product and its underlying. Many speak of having a central repository for OTCs, obligations, etc but this needs more thinking from the regulators and the financial markets. Moreover, the markets should focus more on quality data and transparency.

Eric Benhamou, CEO pricing Partners, sees an evolution of the market as the crisis showed underestimated risks which are now being taken in consideration.

Claude Martini, CEO Zeliade, advocated the need for financial markets to implement best practices for product valuations: buy-side should apply the same practices already adopted by the sell-side and verify the hypotheses, price and risk related to a financial product.

Cornut admitted things have changed since 2005, when they launched DerivExperts and nobody seemed to be interested in independent valuations. People would ask what value they would get from an investment in independent valuations: yes, regulators are happy but what’s the benefit for me?

This is changing now that financial institutions know that a deeper understanding of financial products increases their ability to push the products to their clients. The speech I enjoyed the most was from Patrick Hénaff, associated professor at the University of Bretagne and formerly Global Head of Quantitative Analysis - Commodites at Merrill Lynch / Bank of America.

He took a more academic approach and contested the fact that having two prices to confront is thought to reduce the incertitude on the product but highlighting as this is not always the case. I found interesting his idea of giving a product price with a confidence interval or a ‘toxic index’ which would represent the incertitude about the product and reproduce the model risk which may originate from it.

We speak too often about the risk associated to complex products but Hénaff, explained how the risk exists even on simpler products, for example the calculation of VAR on a given stock positioning. A stock is extremely volatile and we can’t know its trend; providing a confidence interval is therefore crucial. What is new instead, it is the interest that many are showing in assigning a price to a determinate risk, whilst before model risk was considered a mere operational risk coming out from the calculation process. Today, a good valuation of the risk associated to a product can result in less regulatory capital used to cover the risk and as such it is gaining much more interest from the market.

Henaff describes two approaches currently taken from academic research on valuations:

1) Adoption of statistic simulation in order to identify the risk deriving from an incorrect calibration of the model. This consists in taking historical data and test the model, through simulations and scenarios, in order to measure the risk associated in choosing a model instead of another;)

2) Have more quality data. Lack of quality data implies that models chosen are inaccurate as it is difficult to identify exactly what model we should be using to price a product.

Model risk, which as said above was before considered an operational risk, now becomes of extremely importance as it can free up capital. Hénaff suggested that is key to find for model risk the equivalent of the VAR for market risk, a normalized measure. He also spoke about the concept of a “Model validation protocol”, giving the example of what happens in the pharmaceutical and biologic sectors: before launching a new pill into the market, this is tested several times.

Whilst in finance products are just given with their final valuation, the pharmaceutical sector provides a “protocol” which describes the calculations, analysis and processes used in order to get to the final value and their systems are organized to provide a report which would show all the deeper detail. To reduce risk, valuations should be a pre-trade process and not a post-trade.

This week, the A-Team group published a valuations benchmarking study which shows how buy-side institutions are turning more and more often to third-parties valuations, driven mainly by risk management, regulations and client needs. Many of the institutions interviewed also admitted that they will increase their spending in technology to automate and improve the pricing process, as well as the data source integration and the workflow.

This is in line on what has been said at the event I attended and confirmed by the technology representatives speaking at the roundtable.

I would like to end with what Hénaff said: there can’t be a truly independent valuation without transparency of the protocols used to get to that value.

Well, Rome wasn’t built in a day (and as it is my city we’re speaking about, I can say there is still much to build, but let’s not get into this!) but there is a great debate going on, meaning that financial institutions are aware of the necessity to take a step forward. Much is being said about the need for more transparency and a better understanding of complex, structured financial products and still there is a lot to debate. Easier said than done I guess but, as Napoleon would say, victory belongs to the most persevering!

Posted by Sara Verri | 28 October 2010 | 4:50 pm

Transparency Regulation is not Transparent.

Decent FT article on the problems with the transparency of stress testing of financial institutions in Europe.

Posted by Brian Sentance | 9 July 2010 | 2:31 pm

Active to Passive and Back Again

FT article saying that passive fund management is set for growth giving the disillusionment of investors with the benefits of active fund management. Interesting piece was the bit where the growth in index-based investment may ultimately introduce index-inclusion distortions in constituent pricing, so ultimately swinging round to benefit those active fund managers that are still around to see this. Makes sense as there is always some money to be made (and lost!) when everyone starts to do the same thing, or maybe I am already being taken in by the forward-looking PR departments of the active fund managers?...

Posted by Brian Sentance | 27 June 2010 | 6:33 pm

The Humans Between Risk and Data

Some of my thoughts on risk management, data management and human behaviour, are to be found on page 20 of the Inside Reference Data Special Report "Managing Risk"

Posted by Brian Sentance | 21 June 2010 | 1:22 pm

A Crisis Needs a Utility?

I heard Francis Gross of the ECB speak at one of the panel events at the XTrakter Conference last week, and found that I couldn't avoid asking him whether the aims of the "Data Utility" initiative by the ECB could be better separated from the means by which the ECB proposes to solve them. At the moment, reference data issues for the industry and the data utility seem to be presented as a single "package". I can't say that the response to my question was a clear one to my understanding; however I would say that Francis was helpful after the panel had finished and provided a recent presentation of their ideas, of which you can find a copy here.

Looking through the presentation, the motivations put forward for why the industry needs a data utility seem to include:

Data processing must be done in an automated manner, since data volumes have moved beyond the capabilities of manual processing.
- can't see anyone arguing with this
Data is a major bottleneck, with multiple providers/sources each with the own "data dialect"
- agreed and to some extent what keeps data/data management vendors in business, but sounds sensible to standardise if possible as there are plenty of other problems to address
These data dialects lead to increased cost, operational risk and reduced responsiveness
- agreed, mainly a cost aspect I would suggest
The recent crisis was not helped by weak data management in the industry
- but nor was it the cause, so not a great premise for a data utility
- lack of transparency of data
  - "transparency" is an over-used word at the moment, but certainly clarity and quality were/are needed
- systematic risk could not be assessed due to the availability of data
  - using terms like "systematic risk" seems to imply the regulators could calculate something, whereas this discipline is new so I guess we are really talking about simply knowing who is exposed to who and how.
We need the capability to run large scale computing analysis on a vast pool of micro data, sometimes on an ad-hoc basis when a crisis begins
- fundamentally agreed but also good to qualify with what you propose to be calculated - having a set of "numbers" doesn't seem to have helped much recently...

I started the above bullet point list by saying it contains the motivations for "why the industry needs a data utility" but I guess looking at the above list they really point to the more general aim of "why we need better industry-level data management". In the presentation the above points are then used to state:

"We all need the same good basic reference data. Why build more than one infrastructure?"

Maybe "Why build more than one infrastructure?" should really be changed to say "Why maintain more than one infrastructure?" given that Bloomberg, Thomson Reuters, Six Telekurs, Interactive, Markit and all the other vendors already infrastructure to do this. Not sure if I should read anything into the wording but more logical leaps of faith are to follow.

The presentation then moves on to state that shared reference data standards are a must, to which I cannot see many consumers of data disagreeing with that statement. Not sure I agree though with the overly simplistic statement that "Data will be good for all users or good for none". Trying telling that to the accountancy and risk departments for example but I suppose what we are talking about here is basic reference data not the more subjective price and valuation data. Reference data on instruments and entities is either right or wrong, and the presentation makes the good point that no amount of "data cleaning" can help this i.e. if wrong, the data needs to be re-captured from an accurate source.

The call for the establishment and use of reference data standards in the presentation then seems to be used to "slide "into a call for a standard reference data infrastructure. Unless I am very much mistaken, these two things are not necessarily the same thing and so it seems a logical leap has been taken here. The presentation talks about the possible necessity of "top down" legal compulsion for the industry, again something that I could agree and see the need for, but both the issues and legal compulsion do not automatically drive us to a "data utility" as the only option? Why couldn't legal compulsion be applied to the existing data vendors to standardise on common IDs for instance? ISIN is proposed as a standard in the presentation, but I can only assume that this is due to the ECB being mainly focussed on the bond world where to a large degree ISIN's work (i.e. are unique), whereas in the world of equities ISIN needs a lot of qualification (currency, exchange, share class...) before it uniquely identifies a quoted equity.

In summary, the presentation starts with showing how great the ECB's Centralised Security DataBase is (7 million securities, 3 million record updates/day etc...) and it does look good. The data issues for the industry seem clear, although I think the "crisis" is a bit of a red herring to the aim of data cost reduction, however the logical jump from industry need to effectively "we must have a data utility" is an interesting one, one where I would prefer that more options were discussed. It seems ironic that in these days of "transparency" it is not at all that transparent to me why more alternative solutions are not being discussed and a choice justified. Talking of choice and as a final thought, I am also not sure why the data vendors are not up in arms about this initiative - are they frantically lobbying behind the scenes? - do they simply think the utility won't go ahead? - or are they afraid of upsetting the EU? Any insight is very welcome, and maybe more of update from me when I get chance to speak with Francis in more detail.

Posted by Brian Sentance | 4 June 2010 | 7:00 am

Of Grasshoppers and Ants...

...not sure what Martin Wolf of the FT has been drinking or smoking recently, but he has certainly put together a very different way of explaining some of the economic inbalances faced by the world at the moment in his latest article.

Posted by Brian Sentance | 27 May 2010 | 9:09 am

XTrakter Conference

I went along to the XTrakter Annual User Conference in London on Thursday - Good event with some great speakers. Angela Knight, CEO of the British Bankers Association, gave a talk to start off the day. Angela seemed a lot less on the defensive than when I have heard her on national radio here in the UK, usually being interrogated by some journalist who wants answers to difficult questions on the financial crisis and the banks role within it.

Angela said that we were in year 3 of the "crisis" with 2008 being about the banks, 2009 being about governments and politics and 2010 being the year of sovereign debt. I guess she enjoyed saying this but that everyone is blaming "Anglo Saxon Banking" for our problems and yet it was not the banks that contributed to the fundamental problems that Greece is facing.

One major theme of her talk was decidedly Euro-Sceptic in tone, which was that the UK idea of internationality and international trade was different from that of Europe. She perceived that in the UK one of our trading parties is Europe, whereas international trade in Europe was more about inter-European and not world-wide trade - I think that there are elements of truth in this but not sure that Germany industry for example would agree that it is not conscious of truly "global" trade? She said that she was concerned by the rules and regulation being put up by governments, particularly in respect of there being too much and in too short a time.

Angela was an engaging speaker and at the very least her opinions prompt reaction, however I have to end this quick post with the best quote of the morning from Anthony Belchambers, CEO of the Futures and Options Association. Anthony said that current frenzy around political and regulatory initiatives to control the financial markets remind him of:

"A bar room brawl, where the brawlers don't punch the person that started the fight, they punch the person they have always wanted to punch..."

Posted by Brian Sentance | 24 May 2010 | 3:11 pm

Counterparty Event

I went along to a morning panel on counterparty data management on Tuesday, sponsored by GoldenSource, Avox and Interactive Data, and hosted by Virginie O'Shea of the A-Team. Counterparty data obviously has a very high profile currently in light of recent events, however the advice from the panel fundamentally seemed to be get the basics of data management right (ownership, control, consistency, quality, transparency), rather than anything radically new.

There was some debate about the possible extension of BIC (Bank Identifier Code) to be used more generally as a standard for a unique business entity identifier - this seemed to be received well but there were concerns that such an initiative would not solve the problem but rather become an addition to the already complex entity-mapping process.

The "Data Utility" from the ECB was also debated, and it was refreshing to here some negative (realistic?) things said about it, such as the concern raised by Interactive that this might involve huge public spend without necessarily understanding why a new government sponsored entity would be able to do better than existing data providers. Obviously a data provider would say that, but I have to agree, it seems there is too much focus on having a data utility and not looking at the different options for solving industry data issues (one option obviously being a data utility, but lets not pre-package the problem with a solution but more of that in later posts...).

For more detail on the event, then take a look at Virginie's blog post.

Posted by Brian Sentance | 21 May 2010 | 9:56 am

Cloudy definitions

Given that I am English and can tend to start many personal introductions with a short conversation about the weather (generally either "awful" or "not bad for this time of year"...), then maybe I should be very receptive to the use of weather-related expressions in technology such as the "cloud". Maybe not however since the "cloud" and "cloud computing" have reached that zenith of marketing hype, when everyone is talking about a new technology regardless of if they are sure what it actually is (or might be, or could become...).

Anyway, I finally swallowed my cynicism and on Thursday morning went along to "Migrating Business to the Cloud", an event by Microsoft hosted at Bafta (small venue where the UK deals out its equivalent (?) of the Oscars). The master of ceremonies was Mark Taylor of Microsoft, who gave a general introduction to what Microsoft are doing in the "cloud", and of particular note he described the four types of computing scenarios where cloud computing can optimally be applied:

Predictable Bursting - where computing needs come and go in predictable waves of usage/demand
Growing Fast - where computing needs are rising exponentially like in a successful internet start-up
Unpredictable Bursting - where computing demand comes in unpredictable bursts, such as that associated with say usage of a backup computer centre in disaster recovery
On and Off - where you might run a process once a month or at an interval you decide

The above definitions seem ok to me but there is (probably understandably) some overlap in usage cases. The "Growing Fast" case for start-ups is interesting and more of that later.

Mark handed over to David Chappell who gave his perspective on cloud platforms as they are today in the market. David was a very entertaining and knowledgeable speaker, despite wearing a dodgy suit (what happened to those trousers?!) and having a peculiar wide foot stance when speaking. Anyway I digress, on to what he said. David started by saying what the "Cloud" is comprised of:

Cloud Applications - basically this is Software as a Service (SaaS) and some current examples of this would be Salesforce.com CRM, Microsoft Exchange Online and Google Apps.
Cloud Platforms - a platform for developing cloud applications, with the following characteristics that it:
- is aimed at developers for creating and running cloud applications, not end consumers
- provides self-service access to computing resources
- allows very granular, on-demand allocation of computing resources
- charges for the consumption of computing resources in a very granular manner

David then explained that due to its ambiguity he disliked the usage of the term "Private Cloud" in the ongoing debate about publicly available cloud services (such as those provided my Amazon, Microsoft and Google) vs. private clouds deployed within private institutions. David said the main difference was that private clouds do not have the economics of public clouds (i.e. pay for what you use only when you need it). That point seemed straightforward, however I would have thought that with a large global organisation with many different departmental computing demands the economics of a private cloud would be similar to a public one.

David then went on to explain that there are two kinds of Cloud Platform:

Infrastructure as a Service (IaaS) - this is a cloud platform the provides a developer with a virtual machine (VM) that has (almost) full access within it; put another way the development environment gives the developer total control but with that control comes responsibility.
Platform as a Service (PaaS) - this is a cloud platform that runs an application that a developer has created; it is easy to use but has limited control for the developer.

David put forward that there has been only 5 major software technology platforms over the past 50 years:

Mainframe
Mini-Computer
PC
PC-based Server
Mobile

He perceives that the Cloud is the 6th major software technology platform, and as such he is extremely enthusiastic about the opportunity and benefits that this presents to the whole of the software industry and its consumers.

David categorised Microsoft's cloud platform as (mostly) PaaS, which had three main components:

Windows Azure - for environment for running cloud applications within the platform
SQL Azure - relational storage within the platform
Windows Azure Platform AppFabric – (David noted the long name and sympathised with trying to name things sensibly) this provides and manages the infrastructure within the platform

He then moved on to describe the main usage scenarios for Windows Azure, for applications that:

need massive scale, such as Web 2.0 applications
need high reliability
have highly variable loading
have short or unpredictable lifetimes
need parallell processing
will either fail fast or scale fast
do not fit easily in a single organisation's data centre, such as joint venture
need external storage

David said that in the fail quickly or scale quickly scenario, this was squarely aimed at technology start-ups where using Cloud technologies would effectively increase the frequency at which new ideas could be tried out at less economic cost if they go wrong, but are ready to scale massively if they become the new "Facebook" - so much so that many of the VCs in Silicon Valley are now insisting that start-ups use cloud technology as a condition of funding.

Amazon's Elastic Compute Cloud (Amazon EC2) was the first major commercial cloud platform, and David categorised this as IaaS, where effectively you get a Virtual Machine (VM) environment that provides a lot of control but requires more effort to control than an PaaS such as Azure.

David said that he was surprised that the Google App Engine, which has Python and now Java as its programming languages, did not come with any traditional relational storage (unlike most other cloud platforms) but on speaking with Google he found that the storage engine and the whole platform is again designed primarily for Web 2.0 apps and as such storage usage was more about retrieving photos, video etc and less about querying across many records.

David was very complimentary about the cloud platform from Salesforce.com called Force.com, He said that the sales pitch from Salesforce.com would be straight to business users, effectively saying that they could build scaleable, resilient applications without involving the IT department and without needing programming expertise. He asked the audience if anyone had used these tools and a few folks confirmed that they were extremely impressed by what the platform offered.

Bob Muglia (President, Server and Business Tools, Microsoft) then gave a quick talk on Microsoft's plans for Azure. He mentioned how Microsoft's new search engine, Bing, was based on several hundred thousand servers running in Azure, but only had a handful of operating staff in contrast with the usual economics (taken from Gartner) that usually 1 operations person was needed for every 50 servers. He emphasised that Microsoft was committed to the further development of "on premises" operating systems but that Microsoft was totally committed to cloud computing, its development and its support.

He said that some of the tools found in the Microsoft technology suite, such as SQL Reporting Services, are not yet available in the cloud on Azure/SQL Azure (due end of year though) - he said that he hoped that people understood that re-engineering an existing application for the cloud sometimes took time to ensure the scaleable and reliability demanded when providing the functionality through the cloud. The vision put forward by Bob for development of cloud applications seemed very compelling, with Microsoft aiming to make things such enabling resilience for a globally available cloud application as simple as ticking a check-box in Microsoft Visual Studio. He put forward that the major barrier to cloud adoption was the human aspect of trust of moving applications "off premises". He said that he saw a fundamental shift across all industries to cloud development and deployment, but added there may be some areas such as government and finance where this process takes a lot longer.

The event then switched to presentations by EasyJet, RiskMetrics and SeeTheDifference. The head of IT at EasyJet gave his pitch first. His department get an annual budget of 0.75% (small?) of turnover of £2.5bn (larger, so translating to £18.75m) and has around 60 people. He presented how EasyJet has taken an incremental approach to the adoption of cloud computing, utilising both "on-premises" and cloud ("off-premises") technology together (exposing end points of applications into the cloud at first). He advised this approach since it:

was a smaller step than full-blown adoption
was lower risk
demonstrated big value in a short time-frame
leveraged the rich functionality available in Azure
accelerated acceptance of cloud technology

Dr Rob Fraser of RiskMetrics was next up. He explained whilst Moore's Law says that computing power doubles every 18 months, the calculations needed for risk management have doubled every six months. This has driven the need for parallel computing to meet this calculation need, and that RiskMetrics' RiskBurst service uses around 2,500 64-bit Opteron cores in their data centre but combines this with use of Azure to meet the peaks in calculation needed during each day (the similarities with power consumption management were pretty apparent). He said that average CPU consumption was around 18% of peak, hence a combination of both on and off premises compute power was a good solution for them. He mentioned that the management of this hybrid combination of technologies, and in particular being able to show real-time billing for it was a key area of investment for RiskMetrics.

The final presentation was by SeeTheDifference. The main point of this presentation was that this charitable organisation had zero permanent staff involved in IT, but regardless was able to deliver a very professional, reliable and scaleable website using external consultants to build on Azure.

Final section of the morning was a roundtable discussion with questions from the audience. The EasyJet guy said that the human mindset was key to the adoption of cloud computing. In terms of what keeps him awake at night was the thought that what would happen/how would attitudes change if any of the cloud infrastructure failed - so far it has experienced 100% up time. Rob of RiskMetrics was concerned about the stability of the platform, trying to ensuring that any changes introduced do not damage reliability. He added that he disagreed with Bob Muglia and thought that financial institutions would adopt public clouds quickly – he cited their experience of their revenues now being 90% based from service provision not on-premises applications. David said that he took some of the comments from Bob to indicate that Microsoft would also offer more of a pure VM (IaaS) soon in addition to the PaaS approach of Azure. David said that trust was the major issue in cloud adoption and he advised an incremental approach so "get your feet wet" then build from there.

On the whole the presentations were good and my knowledge of cloud technology has improved a bit - certainly it is fantastically appealing to develop globally available applications with no scaling, no resilience or data replication issues - it sounds too good to be true which generally means it is, so I guess there is much more work to do in gaining trust and acceptance for this technology. So my (pragmatic?) cynicism remains - but cloudy days are certainly coming and for a change maybe this is something to very much look forward to.

Posted by Brian Sentance | 17 May 2010 | 8:37 am

Accountants, Prices and Upsidedown Elastic...

I am sure I am not the only one who has had to suffer the boredom of a economics lecture on price elasticity, but my interest in this old topic was sparked by an article by Tony Jackson in the FT on Monday, providing a very simple and clear explanation of how mark-to-market accounting (see earlier post) can conspire with leverage to turn price elasticity on its head, so the more something goes up in price, the more in demand it becomes...perhaps I should have paid more (or less?) attention to what the dusty prof was saying...

Posted by Brian Sentance | 31 March 2010 | 11:35 am

CEP - Part of the technology furniture?

The CEP market is apparently maturing - don't miss this post "CEP: LaserDisc or DVD?" by Adam Honoré at Aite Group with an interesting view of the future of CEP technology.

Posted by Sara Verri | 29 March 2010 | 11:29 am

The Value in Product Control

Good post from Robert Peston on the BBC website on part that the Product Control Group did (or rather didn't?...) play in the problems at Lehman's, according to the official US bankruptcy report on Lehman's by Anton Valukas.The post highlights the report's findings that the Product Control Group did not have the quant experience to keep up with CDO trading desk.

Interesting findings on Lehman's, but variants on this theme seem to be elsewhere too. A contact who knew Merrill's New York trading operation in the run up to the crisis recently asked me how many quants did I think used to work on the CDO trading desk. The surprising (?) answer was not one...

Posted by Brian Sentance | 21 March 2010 | 5:22 pm

Data models are not what they used to be...

AIM have released the results from their 2009 survey on reference data management which is worth a look, particularly given the 2008 results are also shown for comparison. Seems like Mike Atkin and the EDM Council have their work cut out in getting the Semantics Repository adopted if the survey is anything to go by, with the number of institutions using standards-based data models having dropped significantly when comparing 2009 to 2008. What is going on there in these heady days of the finance industry sorting out its data problem through adopting standards? - In cash starved times, maybe it costs more to conform to a standard? - Is the survey data not broad enough? Any ideas appreciated!

Posted by Brian Sentance | 18 March 2010 | 8:09 pm

Risk, Data Transparency and the MBS Market

I spent the morning yesterday over at the FIMA USA event in New York, and caught the panel discussion chaired by Neil Edelstein of GoldenSource. Stand out speakers were Amy Hawkins of BNY Mellon and John Bottega of the Federal Reserve.

Neil started the panel by asking the panel for their thoughts on the current drive to improve "data management for risk". Transparency and quality were mentioned a lot unsurprisingly, with John Bottega adding that he was aware that a lot of banks were now focussed on the data that in the past had been "not available" for risk management, not just the quality of data that is readily accessible. All panelists focussed on the need to manage risk across the whole institution, not just by product silo.

On the topic of data standards and transparency, John referred the audience to testimony on the Mortgage Backed Securities (MBS) market presented to the US Government by the XBRL group. Apparently the filing process for mortgages allows free format filing and so is of little use from an automated processing point of view. John also pointed out that a key piece of data in assessing risk is that the "first time buyer" flag was found to be present in only 15% of the filings.

John also mentioned that if loans and mortgages could be given standard identifiers, then this would enable new levels of risk management - for instance it should be able to extract those obligations against a specific region that for example is experiencing economic recession. These would be the benefits of getting data standards in place.

As was later expanded upon in a later talk by Kay Vicino of Northern Trust, there was a lot of panel discussion on organisational data governance and the management structures needed to achieve it. On the governance side of things then whilst it is not an exciting topic, it is obviously vital - main point seems to be establishing data ownership and responsibilities which brings me back to the point that a lot of (most?) data management issues are down to managing people and organisational politics, not just down to good technology (although it helps!).

Overall a reasonable panel, and the XBRL testimony looks worth a more detailed read (if the testimony link doesn't work then go to the www.xbrl.org site and search for a report called "Using Standards for Transparency")

Posted by Brian Sentance | 17 March 2010 | 4:42 pm

How not to do marketing #1

I ran into this very funny post on the rebranding of Fortis into "ageas". Worth reading (and learning from it)! Also don't miss some of the comments posted for how other banks in the news could be renamed - join the debate and enter your suggestions too!

Posted by Sara Verri | 11 March 2010 | 3:43 pm

One man's speculation is another man's insurance...

The current finanical crisis in Greece has prompted an outburst of entertaining discussion at the FT about CDS contracts, initiated by a feature article by Wolfgang Munchau who advocates that naked CDS contracts should be banned. The main argument used is that you should not be able to insure against a risk that you do not face e.g. buying insurance on somebody else's house then arranging to have the house burnt down. In support of Mr Munchau, one reader letter points out that insurance without interest in the insured item has been illegal since 1746, which on the face of it seems a long enough time to be a credible point in the discussion.

However, in using this argument then Mr Munchau seems be to attacking the whole of the derivatives industry not just CDS, for example the same argument could be used to ban the use of naked index puts to hedge equity market risk. I guess he is also helping some of the politicians in the EU direct attention away from Greece's financial mismanagement more towards the "evils" of the derivatives markets and hedge funds.

Some good letters in response, for instance this one with a good illustration of what hedging would be like without intermediaries to buy and sell risks that they do not own, plus another more direct one from the Association of Corporate Treasurers.

Whilst talking of Greece and credit, the FT Alphaville team also poked some fun at Anatole Kaletsky, the economist of the London Times Newspaper, who has recently done some interesting articles in the paper concerning his predictions about the stresses being suffered by Greece and the Euro. From their post, it would seem that Mr Kaletsky also runs a credit related fund, so it is implied that some of his newspaper views need to "calibrated" against his own vested interests...

Posted by Brian Sentance | 9 March 2010 | 2:40 pm

Data Management Panel

Thomson Reuters held a panel event on data management at their London offices on Tuesday last week, with speakers from Barcap, LCH.Clearnet, DB, Mizuho and Citi. This event was held in follow up to their recent report "Beyond Golden Copy". Below are some of my notes on the summary points the panelists made:

The Value of Data - Kris Bhattacharjee of Barcap said that there were currently two main drivers behind the perceived business value of data; i) Regulators are expecting more information, adding additional requirements and conducting more adhoc reporting requests. ii) Business users/decision makers want more granular understanding of trading and risk management data, in order to decide how best to allocate scarce capital to what trading positions.
Data Metrics - Kris said that the metrics were many but timeliness of data was becoming a key metric - over the past two years regulators have moved from allowing say 2 months as a reporting timeline down to 10 days recently. Additionally timeliness is again vital as regulators demand adhoc reporting in response to market events.
Accuracy/Completeness - Again regulators are driving this, with the "bad numbers in, bad numbers out" as the main motivation. Unsurprisingly, counterparty data is also being required at a new level of detail and accuracy down to a portfolio level in light of the crisis.
Granularity of Data - Deeper granularity of data being driven by scarce capital and the need to understand how efficiently it is being used. Basel II has also driven greater granularity over Basel I. Reflecting what I have heard from some our clients, Kris added that the data associated with securitised products had increased greatly as people need to understand exposure/risk and pricing in more detail (rather than assume blanket statistical behaviour for a whole basket of assets).
Stress Scenarios - Kris again mentioned the understanding of counterparty exposure driving the need for new data sets, as had the initiative of banks having "living wills" to allow a bank to be wound down in an orderly manner.
Everybody has Left the Building! - Martin Taylor of LCH.Clearnet was a great speaker and said that the biggest new problem that the collapse of Lehman's created was that ordinarily there are people around to help with extracting from systems what the exposure is to the various counterparties. In the Lehman's case there was nobody around to help, making the process very difficult and leading to the need for changes to address this problem.
Mandating Data Integrity - Martin added that data security, integrity and auditabiliy were vital, and in particular put emphasis on the people that are running the systems that they have their own form of integrity so that an institution knows that the people can trusted but is also capable to deal with a situation where the people are not around to help. Martin felt that this level of data management should be mandated on the industry and that there was an awful lot that finance could learn from industries such as Pharmaceuticals in terms of product approval and management/robustness of data.
Data with No Cost or Value - Neil Fletcher of DB was another good speaker who started his talk by saying that pre-crisis people thought of data as project based, otherwise dealt with it on an adhoc basis and considered data as having no cost or value. Institutions had a spaghetti approach to data, with systems/projects being process not data based i.e. the systems get only the isolated data sets they need only when they need it.
Quality is Now the Data Driver - Neil said that 18 months on from the crisis, then whilst ROI is still important for data projects then quality of data is the key driver.
Sponsorship and Ownership of Data - Neil added that quality data is an asset as are the systems that produce data quality, and to ensure success data management projects needed high level business sponsorship, but also ongoing and clearly defined ownership of all data sets and their quality.
Enterprise Data Virtualisation - Neil said that DB were embarking on a long term project to ensure that all systems get data from the same logical place on a global basis, and that they were investing heavily in data virtualisation technology as a key means of achieving this goal. DB are starting with reference data, moving to transactional/positional data and on to other data types. For each type/category of data ownership would be clearly defined across all systems and would enable real-time transformation of the data into whatever format it is needed in.
Enterprise Data Model - Neil said that as a result of this virtualisation approach then you have to invest in putting together an enterprise data model for all data used in an institution. From my point of view this could be interpreted as a move back to "big EDM" (with all the project risk that implies) but I guess it is being approach on a more staged manner.
Lip Service to Data has Ended - Neil summarised by saying that lip service to data management has ended with the start of the crisis and that 18 months on the enthusiasm for dealing with the data problem has not diminished.
Publish/Validate/Subscribe - Simon Tweddle of Mizuho echoed a lot of what Neil said in approach to global data management and ownership, but added that he believed that the model of publish/subscribe needs to change to publish/validate/subscribe to ensure data quality.

Most of the panelists agreed that bringing in experience from external industries (Pharma, Oil & Gas, Internet Search etc) would be beneficial since we should not assume that the financial market has the expertise to get data management right first time (take a look at this article from the FT for a related idea). Martin of LCH.Clearnet was convinced that mandated data management would come and would be beneficial, which some of other panelists did not agree with and suggested that the industry needs to get ahead of the regulators to head this possibility off. Simon said that the focus on complex data/products was wrong given that the basics (what is our exposure to this counterparty?) were not being done (not sure I agree with this totally, both are needed given the losses from CDOs etc). Overall it was good panel with some interesting debate and speakers.

Posted by Brian Sentance | 8 March 2010 | 2:30 pm

Beyond Golden Copy?

Interesting reading in a survey put together by Lepus and Thomson Reuters and publicised on Finextra this week. Summary findings:

Data management budgets are increasing, with 77% of firms intending to increase spend on data quality and consistency and 32% saying spend would increase significantly.
Tearing down data silos is a key initiative, 70% of firms are looking to revise data management solutions as a result of the crisis, and 31% of firms cited data quality and consistency as the most important driver.
Data management for risk is the top concern, with 87.25% of firms looking to integrate data repositories in risk, and 62.5% saying that they were close/very close.

This seems to be consistent with another article on Finextra this week, with Deloitte predicting a much greater spend on risk management projects. Putting the marketing aspects aside for a moment, I don't think it is abundantly clear from the actual content of the Lepus survey as to why the title includes the phrase "...Beyond Golden Copy" other than the type of data management they refer to seems to have more emphasis on global/firm-wide data integration than your traditional EDM golden copy data warehouse approach.

It is also interesting to hear so much about consistent data across the entire enterprise (driven by risk and regulation) which seems to echo the "big EDM" projects of old that did prove that successful, and to some degree is at odds with what the likes of Golden Source and Asset Control are currently saying about choosing smaller projects to bite off on rather than the enterprise approach. I would suggest however that there is no issue in having smaller projects in mind so long as they are compatible with the overall goal.

The integration and consistentency of data across front, middle and back office was also interesting, and in particular the front office integration echos some of the things I have been saying about the need for analytics management and the management of front office data as part of the data management process, not something to be ignored in the hope it sorts itself out.

Posted by Brian Sentance | 5 March 2010 | 3:28 pm

Fund administrator or data distributor?

Just caught up with this article appeared on the A-Team website - Bloomberg is facing pressure from the industry with regards to users concerns about its initiative to make its codes freely available (see previous post Truly "Open" Bloomberg?). In the article, Max Woolfenden, managing director of FOW Tradedata, recognizes the potential of the BSYM website but advocates more progresses to be made in order to improve completeness of the data offered and in particular to clarify what exactly 'open' means.

According to A-Team, Bloomberg is also facing pressure with regards to a possible introduction of a new licensing structure for Service Provider Agreement (SPA) contracts for fund administration clients. Under the new system, fund administrators would be required 'to pay per security in each individual client portfolio', effectively changing the status of the fund manager to that of data re-distributor with all the cost increases that implies. It will be interesting to see where this heads - will the administrators simply pass the data costs through to their clients, absorb some costs as a competitive play or simply move away from using Bloomberg data?

Posted by Sara Verri | 23 February 2010 | 5:28 pm

When is a trade, not a trade?...

...er, when it is a hedge? Adding to my current confusion over just how the Obama administration is going to define just what is and is not "proprietary trading", Gillian Tett of the FT today has put together a good article on some of unexpected effects that such a ban may have - my advice is don't mess with the all-powerful "Law of Unintended Consequencies"...

Posted by Brian Sentance | 19 February 2010 | 2:58 pm

"Cut and Paste" Valuation Services

You can talk about more robust modelling, more stringent scenario testing and even moving everything onto an exchange, but unless we move the principles of good data management (in my view: consistency, security and quality of all types of data) into the front office then we will continue to get front-office mis-marking as described in this article in the FT.

Thanks to Ralph Baxter from Cluster7 for highlighting this article for me and those of you interested in this topic of operational risk and spreadsheet mis-use should maybe go along to EuSpRiG this year, and maybe take a look at a paper Xenomorph presented at a previous conference.

Posted by Brian Sentance | 4 February 2010 | 9:49 am

More Products, Less Complexity?

Decent article(and title!) explaining ETFs in FTfm today - growth of the market sounds impressive, from $40bn in the year 2000 to over $1,000bn under management now. Seemed like a bit of a day for new financial products in the FT, with the LSE announcement of opening up direct bond trading to retail investors through offering corporate bonds issued in sizes well below the usual £50,000 size (and catching up with more usual practice in Europe). Whilst not a retail product (I guess some of us already have life insurance?), longevity derivatives seem to continue their rise too in liability driven investment.

Meanwhile over on Linkedin, Structured Products magazine are asking just what constitutes a "complex" product? A decent question since complex products are not necessarily risky, but certainly "complexity is in the eye of the beholder" is most likely answer in my view - echoing a growing problem in finance, regulation and economics at the moment; there are too many people searching for the unique "right" answer to questions that simply do not have one. Maybe we should stick to the answer to everything being "42" and give up the search for the question?...

Posted by Brian Sentance | 2 February 2010 | 1:51 pm

RiskMinds - VaR as simple as chartism?

Interesting panel debate at RiskMinds Wednesday morning, entitled "Sophisticated Complex Models vs. Crude Robust Risk Measures".

Riccardo Rebonato of RBS started off the debate in (untypically?) controversial style by saying that he thinks that the risk management models (mostly VaR) used in financial markets are peculiar. Peculiar in that coming from a physics background he is used to models that have "causal" links between inputs and outputs, whereas VaR is based simply on the P&L distribution of a portfolio i.e. all the information is contained in the data itself. Riccardo said the obvious analogy was with chartism, where decisions are made on the observed market data itself without any reference to external (exogenous) factors at all (perhaps he should have a discussion on endogenous risk with Jean-Phillippe Bouchard at Quant Invest). Riccardo suggested that in the range of models from those that are "over specified" with two many inputs to those in "reduced form", then VaR was far too much at the reduced form end.

In response to Riccardo's proposal that risk models should involve more causal ("factor") effects, Andreas Gottschling of Deutshe Bank countered with the quote from Harry S. Truman "Give me a one-handed economist! All my economists say, On the one hand on the other.". To which Riccardo acknowledged that maybe Economists and Econometrics were less suited to trading/analyst reports (e.g. give me a single view of what the prospects/returns will be) and more suited to risk management (e.g. give me a range of scenarios with supporting assumptions for each).

Chris Finger of RiskMetrics moved on to put forward an argument for standardisation of risk reporting, saying that it was impossible to say what methodology was behind the VaR numbers disclosed by major financial institutions. He proposed that risk reporting needed to be standardised and obligatory, but emphasised that risk management should not standardised. Paul Shotton of UBS agreed, saying that whilst micro-prudential risk of Pillar I had decreased risk on an individual institution level, it had increased systematic (macro) level risk and this was an area of failure for the regulators. On this the panel agreed, echoing a lot of what Avinash Persaud said in proposing the more diversity of risk management was highly desirable.

On standardisation, Riccardo noted that many banks had switched from using 10-day to adjusting up a 1-day VaR, and as a result presenting a less risky picture to analysts and regulators, regardless of how risky the "tail" of each institutions' P&L distribution is. Riccardo also proposed that there should be "constructive ambiguity" over what is asked of the banks by the regulators - put another way he suggested the regulators should come up with the "curriculum" for risk but not the "questions", as definitive questions encourage arbitrage.

Andreas then brought the debate back to its title, and put forward that maybe VaR should be replaced by simpler measures such as limits on notional traded. Paul suggested that VaR was only good for simpler products and portfolios, under "normal" market conditions. He said that he had been an advocate of more stress testing for a long time as a complimentary approach to VaR, but also combined with the simpler approach of limits.

It was an interesting debate, particularly with Riccardo's proposal on VaR being too simple a measure based on statistics, and wanting a more "causal" model to be developed. Using the example of June 2007, Riccardo said that everyone knew something big was about to happen but this was not reflected in VaR calculations since they are statistically based and inherently backwards-looking and not predictive. The lack of prediction is a very valid point, but putting forward a counter-view, then I get the argument about economists giving a range of outcomes, but surely these should be fed into the scenario engine rather than trying to develop econometric models of relationships between market variables. Econometric models are just as vunerable as any other to the mis-behaviour of markets (anyone seen a stable correlation lately?).

A few of the other risk managers there expressed other views, from the more buy-side folks who were more comfortable with factor-based modelling, to risk managers who said that VaR was already "structural" with explicit relationships between valuations and interest rate inputs for example. It would be good to understand more of Riccardo's ideas on this, since it appeals from making risk a more "forward-looking" process but I find it difficult to quite grasp what "causal" model you can have of markets that is itself robust to changes in market behaviour.

Posted by Brian Sentance | 11 December 2009 | 4:52 pm

RiskMinds - The Failure of Risk Models

Avinash Persaud of Intelligence Capital gave the opening talk of the morning at RiskMinds (see first of set of posts from last year here) and put forward a lot of the very good ideas that he has contributed to in the recent Warwick Commission Report. Main points that Avinash made:

Regulators were admirably quick in working out where past regulation had gone wrong in focussing too much on micro (individual institution) rather than macro (whole market)/systematic risk.
The regulators then came out with promising papers on counter cyclical regulation and other positive ideas.
These new ideas do not win votes however and do not satisfy the public's desire to punish someone - Avinash called this the "Bad Apple" policy, with "bad bankers, bad products, bad jurisdictions" being the perceived guilty parties.
All past crises have resulted in demands for three things: i) more risk management; ii) more regulation; and iii) more transparency.
These are fine as demands but evidently do not prevent financial crises.
Avinash recalled his work back at JPMorgan in the early 90's when the 4:15 report was produced for Sam Weill, which eventually led to VAR reporting becoming widespread.
He then fast forwarded to the Asian crisis of 97 where he saw the failings of VAR (or rather its widespread use) first hand with all players using VAR which when volatility increased caused an increase in VAR causing JPM (and all) to sell causing markets to fall, increasing vol causing more selling, increasing correlation and leading to what is called the "loss spiral".
In light of the recent crisis, Avinash said the public perception is that bankers created a load of toxic bombs (products), through them at an unsuspecting public and ran away...
...and in his opinion the reality is that banks created a load of toxic bombs and ran straight towards them i.e. this was a failure of risk management where bankers did not understand the risks they were buying and selling.
He then took us back to the 1950's and the formation of modern portfolio theory with Markowitz and Danzig working at the RAND Corporation.
At that time banks and insurers were still separate, with FX and capital controls still in place meaning that not only could the "efficient frontier" of investment portfolios be observed but it could also be acted upon.
Now everyone has the same information everyone can observe the efficient frontier of investment opportunities but cannot exploit or act upon it, since usually everyone moves in (the "herd") and the value observed is changed by this crowded participation in the market. Here he seems to be echoing a lot of what Bob Litterman said at QuantInvest last week over the "crowded trade" and that the barriers to market knowledge and our ability to act on this knowledge have been lowered forever.
Avinash put forward that many of the models we use today assume the statistical independence of decision making process whereas the reality is that the market is homogenous (everyone is thinking/acting the same) and hence these models are invalid in this "crowded" context.
In light of this, the problem of risk management is not about exogenous risk (risks from outside the market, from Black Swan events to normal distributions) but more about endogenous risk i.e. peoples behaviours upon seeing opportunities cause strategic risks. (Interesting given Jean-Phillippe Bouchard at QuantInvest commenting on what makes prices move). Put another way, behaviour is the issue not the financial instruments themselves.
Avinash proposes that risk capacity (the ability of an institution to absorb a particular type of risk) shoudl be thought through more fully, with for example insurance and pension institutions with long-term liabilities having a much greater capacity to absorb liquidity risk than banks, and banks with short term funding being a better position to manage a loan book.
He pointed out that regulation that uses market prices to protect us against movements in market prices is doomed to failure before it starts.
Booms occur due to some perceived "paradigm shift" technolgy leading to dramatically improved risk/return ratios - he cited things such as cars, electricity, rail, dotcom and the mantra from those involved that "This time it is different..." (see "bubble" post from last year)
Avinash thinks the regulators are significantly to blame for the last crisis since they themselves said the latest financial innovations in credit derivatives were making us safer through sharing out risk in the system.
He said that there is no theory for making a complex system "safe" as a whole and that the regulators did not/do not "get" this idea.
Diversity of approach and risks in a large systems (macro financial markets) is our only current defence and regulatory "best practice" has driven conformity not diversity in the market, making systemic risks higher not lower.
So the regulators are themselves creating a homogenised market.
In terms of solutions, he proposes that risk and audit committees need separating so that risk management does not become a "tick box" exercise.
He further proposes that the risk management function is given some capital so that it can place hedges at a macro level for institution (i.e. looking at the resulting risk when divisional risks have been aggregated) - here is proposing moving to risk "management" as opposed to the much more common risk "reporting" found in many institutions.
One risk management indicator idea he proposed was to put a portfolio management model together that was linked to VAR in order to see where the "herd" is moving to (e.g. low vol, high return Asian markets of the past etc) and to move or hedge against this.
He is concerned that applying Basel II regulation to the Insurance industry with Solvency II will mean that all players will be dancing to same VAR tune which will introduce more risk as more institutions are forced to react in the same way to market movements and volatility.
On the same lines, Credit Rating Agency regulation will create barriers to changes in ratings methodology in response to endogenous market risk, again meaning that everyone will be forced to behave and act in the same ways.
He summarised that "endogenous risk" (movements in the market caused by the market) and not statistical distributions that are the key issue and diversity is the only solution.

Entertaining speaker with some interesting ideas that fly in the face of much of what is being done by the regulators today, and generally well received by many of the risk managers present. Behavioural finance and the "crowded trade" (i.e. everyone doing the same thing in the market causing movements within the market) seem to be key themes occuring in a lot of what academics and practitioners have said on risk management recently. Now what to do about it? Not sure that less (not more) regulation will find many fans at the moment...answers on a postcard please!

Posted by Brian Sentance | 8 December 2009 | 10:04 pm

It's in the hormones...

Taking the discussion on behavioural finance and news analytics a scientific step further, then this article in the FT today on how increased testorone equals an increased appetite for risk taking is interesting. Apparently experience of trading is also a big help in increasing a trader's Sharpe ratio, from which the authors suggest that markets are not efficient and the EMH does not hold. Now if only they could find a hormone that was correlated with increased returns, then I think they'd really have something...

Posted by Brian Sentance | 25 November 2009 | 7:12 pm

Views on Fair Value...

Busy week last week for events in London, this time over at the Goodacre / Six Telekurs on Thursday morning. Guy Sears of the IMA was chair of the event, and the event did have a "buy-side" focus to it. Richard Newbury of Six Telekurs started the event and made the following points on the current state of regulation:

UCITS IV - Richard cited the stats that there are around 37,500 funds in the EU with average value of approximately $180M each as compared to only 8,000 funds in the US with average value over $1B. Richard said that such a proliferation of funds was costly and the more EU could standardise funds and their ability to be transacted everywhere in the EU the better.
Reg NMS - Richard took a little humorous dig at US regulators when he reminded us that Congress authorised the SEC to form a "National Markets System" in 1975 and so this had taken around 30 years to implement. Whilst Reg NMS is often compared to MiFID, he said that Reg NMS had led to consolidation in the US while obviously MiFID has led to fragmentation in the EU.
Hedge Funds - Both EU and US regulators are looking at the hedge fund industry. He mentioned the battle the UK was having with some of the (misguided?) regulation that the EU is trying to introduce with over 30,000 HF related jobs in London. The new regulation is likely to increase reporting requirements leading to more need for regular, standardised fair value reporting.
Credit Rating Agencies - Richard mentioned how there will be more ratings and more ratings types, and the regulation introduced to ensure the CRA do not fall into the conflict of interest trap.
Data Management - He mentioned the importance of data management within what is happening in the industry and noted how the profile of data management was on the increase.

Mike Jenkins of Ernst & Young tried his best to make the accountancy treatment of derivatives interesting and didn't do too bad an effort but I only took the following few notes from his talk:

Unlike US GAAP with FAS 157 there is no single standard Fair Value (FV) definition in IFRS, and unsurprisingly IASB are addressing this.
Mike spent some time mentioning Level 1(quoted), Level 2 (observable) and Level 3 (unobservable) pricing inputs for securites, taken from the IASB exposure draft ED/2009/5 (also see Rowe in earlier post)

Matthew Cox of BoNY Mellon Security Services then gave his presentation on the difficulties/challenges of providing a valuation service to their asset management clients:

His division often have a "2 hour" window to produce valuations for NAV reporting, often for a 12 midday valuation
Data exceptions for investigation went through the roof this year due to increased volatility (comment: didn't get chance to ask whether the validations set were "normalised" for market volatility i.e. a price movement threshold would not be fixed but rather be multiplied by a factor relating to recent volatility levels)
Matthew was very complimentary about the efforts his team put in to cope with this increase in data exceptions.
He mentioned how many of his clients of established "Fair Value Committees" over the past couple of years, comprised of staff from compliance, risk management, portfolio management etc.
Matthew mentioned the importance of time zones in valuation and the timeliness of data, with the availability of intraday CDS prices contrasting with bonds who price only from the evening close of the day before.

The panel debate was moderated by Guy Sears, and included the above speakers plus Nigel Reynolds from TD Waterhouse):

Matthew said that his division sometimes shared the "consensus" price from other clients when one client is looking for some guidance.
He mentioned that a key timeframe in establishing FV was establishing what is a "reasonable" time frame for sale of a security.
Nigel Cox said that "suspended stocks" had been a real issue over the past year, where the client "context" (position, situation etc) would very much determine what value a client would want assigned to a holding.
Guy Sears suggested that valuations should be provided with a confidence interval and not just as a single price
Mike of E&Y said that this is what full disclosure now requires, other memberrs of the panel suggested this was realistic but not what clients (humans?) expect to receive - they want a single number.
Guy wondered whether it was an issue that one entity might value an asset at a value X whilst another would value the liability at Y (not equal to X)
Mike of E&Y pointed out that this was an issue in that current accountancy rules allow a security to be reclassified from "fair value" pricing to "historic cost" basis - this discretion is being removed in future rule implementations
One member of the audience pointed out that Bloomberg, Reuters and Markit were all trying to extract more revenue from data used for valuation purposes.
Matthew advocated that the market needed more competition between niche data vendors such as Markit and SuperDerivatives to ensure innovation in service and more competitive pricing.
The audience asked Guy of the IMA whether the association should have offered more guidance on fair valuation process and best practice.
Guy said they have provided some, but he advocated that trade associations should not have opinions, since it was not healthy to have the asset management industry collectively herding towards the same valuations.

Well attended event with some good speakers, particularly Guy Sears as host was funny, knowledgeable and kept the other speakers on their toes. I would say the most interesting point was still that "opinions" form prices, opinions formed in the investment/funding "context" of the party with an interest in valuing a security - conceptually this seem to make the asset servicing companies a little uncomfortable since what they are contracted to do is to provide the "right" set of numbers by their clients. Human beings feel more comfortable fixating on a single number than a range of possible outcomes/results it would seem!...

Posted by Brian Sentance | 17 November 2009 | 10:48 am

Paying a margin call for the grim reaper...

Seems that liability driven investment and the use of longevity derivatives is set to rise for pension funds, according to an FT article about a survey by Aberdeen Asset Management. Maybe Deutsche Borse was ahead of the game with real-time death data...

Posted by Brian Sentance | 15 November 2009 | 8:54 pm

It's in the news...

I went along to the Forum on News Analytics over in Canary Wharf on Monday evening, organised by Professor Gautam Mitra from OptiRisk / Carisma at Brunel University. We seem to be in the early days of transforming news articles into quantifiable/machine-readable data so that it can be processed automatically/systematically in trading and risk management. It was a good event with both vendors and practititioners attending so was reasonably balanced between vendor hype and the current state of market practice.

As background on what is meant by news analytics data, then for example you might count the number of news articles about a particular company and look at whether the quantity of news articles might be a predictor of some change in the company's stock price or volatility. Moving on from this simple approach (assuming that you are clever enough to be certain about what news is about what company), then you can then move towards assessing whether the news is negative, neutral or positive in sentiment about a company/stock.

The context here is about having the capability to automatically process/analyse any kind of text-based news story, not just those from research analysts that might be nicely tagged with such quantifiers of sentiment (see http://www.rixml.org/ on xml standards for analyst data). The way in which the meaning of the text is "quantified" uses some form of Natural Language Processing.

The event started with a brief talk by Dan di Bartolemeo of Northfield Information Services. I hadn't heard of him or his company before (maybe I should pay more attention!) but he seemed a very solid speaker with strong academic and practical background in investment management and modelling. He referenced a few academic papers (available via their web site) on news analytics, and how news analytics and implied volatility could provide better estimates of future volatility than implied volatility alone. He also made some good points about how investment "models" are calibrated to history and how such models need to adapt to "today" - he put it as "how are things different now from the past?" and put forward the idea of a framework for assessing and potentially modifying a model to respond to the "now" situation. He also suggested that the market can react very differently to "expected news" (having a range of investment "what ifs" planned for a known earnings announcement) as opposed to unexpected information (we are back into the realms of the Black Swan and the ultimate in uncertainty wisdom from Donald Runsfeld)

Armando Gonzalez of RavenPack then began by explaining how RavenPack had become involved in applying text analysis to finance (it seems the subject has its origins, like a lot of things, in the military). RavenPack seem to be highest profile quantified news vendor at the moment, and whilst Armando is obviously biassed towards pushing the concept that money can be made by adding quantified news data to trading models, he said that not many firms are as yet systematically processing news and most people are relying upon manual interpretation of the news they buy/use. Some of the studies Ravenpack have on market news and prices are very interesting, showing how a news event can take up to 20 mins before the market settles on a new "fair" price level for a stock. Additionally, and maybe an interesting reflection on human behaviour, was that in bull markets there are usually twice as many positive stories about companies than negative, but strikingly in a bear market there was still almost equal amounts of positive and negative news - so humans are basically optimists! (or delusional, or just plain greedy...take your pick!)

Mark Vreijling of Semlab followed Armando and suggested that a lot of their sales prospects understandably desire "proof" of the benefits of adding quantified news to trading, but this was a little ironic since most financial institutions have been paying to receive "raw" news for years, presumably because they perceive beneift from it. Mark also mentioned that the application of quantified news to risk management was a new but growing area for him and his colleagues.

Gurvinder Brar of Macquarie then went into some of the practicallities of quantifying and using news in automated trading. He suggested that you need to understand what is really "news" (containing information on something that has just happened) and what is merely an news "article" (like a "feature" in a magazine etc). Assessing relevance of news was also difficult and he added that setting a hierarchy of what kind of events are important to your trading was a key step in dealing with news data. Fundamentally he suggested that why wait for five days for analysts to publish their assessment of a market or company-specific event when you could react to the event in near real-time.

The event then went into "panel" mode where the following points came out:

Dan thought that a real challenge was integrating quantified news with all of the other relevant datasets (market data, but also reference data etc)
Armando picked up on Dan's point by giving the example news about Gillette which at one point was about Gillette the company but then on acquisition became news about the Gillette "brand" which became a part of Proctor and Gamble.
Dan said that a key problem with processing news was also understanding what news was simply ignored by the news wires i.e. we know what is being talked about, but what could have been talked about, why was it ignored and is it (even so) relevant to trading?
Mark and Armando said that the "context" for the news story was vital and that market expectations can turn many "negative" news stories into positive outcomes for trading e.g. the market likes bad news when it is not as "bad" as everyone thought.
Dan made a very interesting point about trading in terms of categorising trades as "want to" trades and "have to" trades. He gave the example of a trade being observed that seemingly has no news associated/prompting it - so does this mean the trade is occuring because somebody "has to" make the trade (a fund facing an welcome client redemption for example?) or because there has been some information leak to a market participant and such a participant "wants to" make a trade before the news becomes available to the market as a whole.
I think all of the panel members then collectively hesitated before answering the next question from the audience, with Microsoft having one of their "text search" R&D team (think Bing...) asking about news categorisation and quantification.
Dan also mentioned something that I have only recently become more aware of, which is that apart from major markets in the US, most exchanges world-wide do not publish whether a trade was a "buy" or "sell" trade (they just publish the price and transaction size). Obviously knowing the direction of the trade would be useful to any trading model, and Dan referred to this as wanting to know the "signed volume".
A member of the audience then asked whether most quantified news had been based on just the English language and the concensus was that most was based on English, but Natural Language Processing can be trained in other languages relatively easily. A few members of the panel pointed out that all languages change, even English, requiring constant retraining, and also that certain languages, countries and cultures added further complication to the recognition process.
The next question asked was whether the panel could outline the major areas that quantified news is applied in - the answer included intraday (but not quite real-time) trading, algorithmic execution, lower frequency portofolio rebalancing and in compliance/risk/market abuse detection.
A good debate ensued about whether "news" was provided by the official newswires or by the web itself. The panel (and audience) concensus seemed to favour the premise the news wires are the source of news and the web is a reflection/regurgitation of this news. That said, Gurvinder of Macquarie gave the nice counter example of the analysts/news wires not making much of the new Apple iPod, when looking at the web it was possible to see that the public were in contrast very enthusiastic about it.

Overall an interesting event. I think the application of "quantified news" to risk management is interesting - maths and financial theory is very interesting but markets are driven by people's behaviour and if "quantified news" can help us understand this better it has to help in avoiding (some!) of the future problems to be faced in the market.

Posted by Brian Sentance | 12 November 2009 | 12:06 am

Truly "Open" Bloomberg?

Interesting couple of articles from Inside Reference Data and Inside Market Data. The first is on Bloomberg making its codes freely available to all from its website http://bsym.bloomberg.com - given past standards-based attempts like ISINs falling short of providing the industry with unique and useful security IDs this looks to be a welcome addition. This seems to be a publicity "win" for Bloomberg, especially given rival Thomson Reuters has recently got some indifferent publicity with the EU over RIC licensing (see article). No prizes for anyone who thinks that Thomson Reuters will not respond in some way with regard to RIC usage, maybe giving us two working proprietary standards that go "open" - at least everyone would then be matching up Bloomberg Tickers and Reuters RICs in public rather behind closed doors - and maybe a good opportunity for a Wiki site to do the matching up?

The second relates to Bloomberg providing a open-source data distribution system called "The Platform", I presume as less expensive alternative to Reuters RMDS. Meanwhile Reuters is busying itself with the plans for its competitor to the Open Bloomberg terminal with "Project Utah". Obviously Bloomberg is comparatively unproven with regard to systems provision so this is a big change and will be very interesting to watch - from a technology point of view but also culturally since can Bloomberg turn away from thinking in "Terminals" all of the time?

Posted by Brian Sentance | 3 November 2009 | 3:52 pm

Shipping Fair Value...

...seems like the shipping industry is as about as confused as the finance industry about establishing "fair value" for assets according to this article in the FT.

Posted by Brian Sentance | 29 October 2009 | 9:13 am

Integrated Data and Analytics Management

Xenomorph was one of the sponsors on the “Integrated Data Management” webcast last week, hosted by Inside Reference Data (audio recording available here). There were a number of interesting questions that arose from the Webinar.

One fundamental although somewhat academic question was "What is Integrated Data Management?". Certainly everyone seemed convinced that there would be less "Enterprise Data Management" (EDM) projects in future, given the expense, scope and scale of such projects. The concensus was that whilst the need for data management was better under stood across all financial institutions, data management projects would be bitten off in more manageable chunks by asset type, business function or division (so are silos back in fashion I ask myself?!). Coming back to the original question, I guess my slant on Integrated Data Management is that we are seeing more and more data management projects that have an integrated reference data and market data elements to them, primarily driven by the need to sort out data quality/completeness/depth for use within risk management (in light of the financial crisis).

Related to risk management, a topic I pushed was that given the origins of data management for STP/back office, and given the interest in low latency tick data management/analyis in the front office, there seems to be a market gap (particularly in the US?) on how to manage data such as IR/credit curves, volatility surfaces and other derived data sets. These data sets seem to fall into the gap between what is thought of as market data (primarily just prices) and what is reference data (IDs and terms & conditions). This is another area where a more integrated approach to data management would be beneficial, particularly in making all these datasets available for risk management.

Coming back to a "hobby-horse" of mine, then I also raised the issue that whilst it is fine to be doing great data management (high quality, complete datasets etc) what is the point if all of your data is ignored by the front office and Excel is used to download the data traders and risk managers need from Open Bloomberg. I think the management of unstructured data (spreadsheets, word docs etc) needs to be elevated as an issue since this (unfortunately?) is where most data resides currently, despite what we data management professionals like to think.

I also think that the principles of good data management (centralisation, quality and transparency) could apply to other things and not just raw "data", but what about centralised pricing and valuation, centralised curves and centralised scenarios for risk? Again what is the point of doing good data management if the ultimate "information" (e.g. a valuation) is done using poor quality data, with a complete lack of transparency over the data and model used.

A good question was asked about models, which was that given pricing models and their weaknesses have formed some part of the recent crisis, do we need more complex models. On having a few conversations about this and thought about it some more, then some would say it is complexity that got us into the crisis so this is the last thing we need. My view is that we do not necessarily need more complex pricing models and valuation techniques, but we certainly need more robust ones which does not necessarily imply more complexity. Coming back to a point raised by David Rowe previously, then I think all quants and risk managers should think about a "second means of valuation" for all the theoretical models they use, and that hedgeability (see recent post on pricing model validation) seems to be the common theme in producing more robust pricing models.

Posted by Brian Sentance | 21 October 2009 | 9:32 am

Pricing Model Validation: Mitigating Model Risk

I managed to catch some of the day yesterday at the "Pricing Model Validation: Mitigating Model Risk" conference. I thought it would be worthwhile going along since firstly the past 12-18 months have made model risk very topical (take a look at previous posts from Riskminds, the Modeller's Manifesto and Wilmott/Rowe).

Secondly more of our clients are looking at managing and centralising pricing models/curve calculators in addition to just managing the underlying data (see this Insight Investment client case study for a recent public example). I am calling this "Analytics Management" which is the business-focussed technology stack that combines pricing models/calculators/analytics with all of the "Data Management" underneath. But enough of my thinly-veiled positioning statements...and on with some of the (hopefully) useful content from the conference outlined below - maybe scan the headings in bold below for those talks of interest but I would particularly recommend the ones by Tanguy Dehapiot and Yuyal Millo...

Model Risk 2009 defining and forecasting. First speaker was Professor Phillip Sibbertsen of the University of Hannover on defining and measuring model risk. Phillip started by saying that "Model Risk" was a new category of risk within the confines of "Operational Risk", and that operational risk as defined by the regulators does not yet currently include the "model risk" of market risk and credit risk, nor the "model risk" of the operational risk model itself. (I am sure I could write that up better!...). Phillip put forward that model risk is not formally a "risk" since it has no probability distribution and that he suggested it should be thought of as "model uncertainty". He also clarified that model risk applies both at the large, portfolio scale (e.g. choice of VAR model etc) and at the smaller, instrument level scale (i.e. pricing of derivatives).

Additionally in terms of measuring model risk then he excluded human failure from model risk measurement since in his view this was difficult to quantify - this approach did not meet with the approval of some of the audience were questioning how this could be excluded from a practical point of view. Phillip's colleague, Corinna Luedtke, then presented some work they had done on calibrating different GARCH models to observed data and showing how even a poor model could produce reasonable forecasts of risk if the time period was short. The work was interesting but again the audience highlighted that the human choice (failure?) in choosing the set of models to try was part of "model risk" and should not be excluded from the definition of model risk.

Is a model accurate? Testing the implementation of a model. Second speaker was David Chevance, Head of Equity & FX Model Validation at Dresdner Kleinwort. David outlined the different sorts of model risk: mathematical errors, missing risk factors, divergence from industry practice, model inconsistencies and implementation risk. He then outlined the sources of these risks: bugs, approximations, numerical precision, numerical boundaries and limitations on numerical methods (e.g. Sobol numbers in high dimension monte-carlo simulations).

David said a key area to start with in validating a model implementation was the front-office documentation of the product, its inputs and payoffs, its pricing model but also details of calibration methods used/needed etc. He made the point here that the documentation can sometimes specify just the deal, but sometimes can express the pricing methodology and pricing parameters. The emphasis was on completeness, accuracy and making use of all of the information available in the documentation. Obviously the ability to review the code used to implement the model was also necessary.

He discussed the trade-offs between a simple validation approach in terms of speed and efficiency of resources against the more time-consuming, resource hungry but more accurate approach of full replication of the model. He also suggested that in choosing a method of validation it was important to balance resource demands against what is actually being validated: payoffs from a single trade, a type of pricing model or a family of financial products. Desired accuracy of the validation was also important, given the trade-off between accuracy and effort, and the fact that small bugs are much more common than large.He finally discussed model version control, the necessary discipline of documenting changes and regression tests for new models, and the regular cycle of model review. Overall it was an interesting talk with a good practical focus.

Practical aspects of valuation model control process. One of the most entertaining and interesting speakers of the day was Tanguy Dehapiot, Head of Validation and Valuation, Group Risk Management at BNP Paribas. He started by referring to a few documents "Supervisory guidance for assessing banks’ financial instrument fair value practices", April 2009 (BCBS 153) which was then implemented within “Enhancement to the Basel II framework” (BCBS 157). The first part of his presentation was around these documents and what the regulators expect to be in place, so I guess the best approach is to read them (the BCBS 153 document content is only 12 pages long, quite short for a regulator!)

Tanguy pointed out that in his view "Mark to Market" and "Mark to Model" are often misleading as both are often required. He prefers the term "Valuation Methodology". He proposed four valuation modes: Direct Price Quotation, Use of Similar Instruments, Risk Replication, Expected Uncertain Cashflows (NPV) and categorised a useful hierarchy/matrix of which financial products fit into which valuation mode and for what purposes. Within model risk, he split off judgemental errors (choice of model etc) as part of market risk and credit risk and operational errors (model implementation and coding) as more definable and avoidable parts of operational risk.

He had some interesting slants on data, saying that he had been surprised that even getting all of the static data necessary to price simpler instruments like bonds had proven difficult. He outlined how model parameters are often stored across a variety of systems (curve definitions in one place, pricing methodology somewhere else) implying to me that this is sometimes difficult to pull together and needs some centralisation to improve transparency around this.

His opinion on market parameters (both observed prices and derived data such as implied volatility surfaces) were often stored in a larger central database but warned that this market parameter database needs to be reviewed as part of the model validation process since some of its data is derived (i.e. calculated, maybe using a model!) and as such should not be taken as perfect for all time and for all purposes. He said that it was important to categorise the origin of data and suggested the following types:

Quoted on an active exchange
Actual private transaction in an active market
Tradable broker quotes
Consensus prices from market makers
Non-binding indicative prices from market makers
Counterparty valuation, collateral valuation
Actual transactions in inactive market

Tanguy proposed that there should a valuation matrix for each instrument, where there might a different valuation methodology used for end of day valuation verses intraday, for risk or for trading, for pricing individually or within a portfolio reval. I guess here the rational is appropriateness, efficiency and transparency about what needs to used when. He also added that he disliked the term "Model Validation" since it seemed to imply that a model was "valid" and preferred "Model Approval" to cover the decision to use a model and "Model Review" to cover model analysis. He said he found managing the "stock" of existing models (and keeping up with when to review them) more difficult than managing the "flow" of new models and products.

Overall Tanguy was a very interesting and funny speaker with lots of practical insights and a fair amount of opinion thrown in, which is always good in my view.

The usefulness of inaccurate models: Financial risk management "in the wild". This talk was given by Dr Yuval Millo of the London School of Economics and he focussed on the evolution of the use of the Black Scholes Merton (B-S-M) model at the CBOE and how the model came to be the means by which the whole options market "communicated". Yuyal is a social scientist and prefaced his talk by stating that "Social Sciences are good at predicting the past"

First thing I didn't know (amongst the many things I do not know...) is that the B-S model was not published until a couple of weeks after the CBOE started trading stock options in April1973. Yuyal said that initially the B-S-M derived prices were not accurate at all (around 25% off the market price on CBOE) and that the model was based on assumptions that plainly were not the case on the exchange (only calls available, no short selling, no continuous trading). The model was used by local Chicago trading firms and the story goes that Fischer Black sold large paper "sheets" of option pricing matrices to these traders (there being no calculators/PCs/mobiles around at the time).

As the markets developed, larger East Coast banks entered the market with stocks being held and traded in New York and options being traded in Chicago, so trading became geographically dispersed. This started the need for "early morning meetings" to discuss the market and the B-S-M model and its parameters became the "lingua franca" or means of communication of options market participants.

He described the first years of the Options Clearing Corporation (OCC) which was set up to ensure that the financial obligations of options and buyers were met. Around 1979-80 the OCC worked overnight to calculate margin requirements, based on the (now?) arcane idea that different margin amounts should be associated with different option strategies (straddles, butterflies etc) and the job of the OCC was to take a portfolio of Option and optimise which combination of strategies would minimise the margin required for the whole portfolio. He said that there were disputes between traders and the OCC around margin levels and difficulties for the SEC with updating their Net Capital Rules as each new option strategy was created. Eventually, the OCC adopted the B-S-M model and implied volatility as the means of calculating margin against market value which enabled them to move away from the operational difficulty of strategy optimisation.

So the B-S-M became the way in which traders communicated about the market but also the model became vital operationally within clearing for the market. By 1987 B-S-M had become the de-facto standard for the market, with the model driving the market in turn driving use of the model. During the Oct '87 crash the model proved to be very innaccurate but the use of the model did not diminish - maybe pschologically the market participants needed a model (even a wrong model) to make communication easier.

I found this talk very interesting and members of the audience asked whether any similar analysis was going to be done on the Gaussian Copula model used to price CDOs. Yuyal said that one of his colleagues was undertaking this research currently. Given that he seemed to be very positive about the use of the B-S-M model within options markets I asked whether he had any opinions on Taleb's criticism of fiancial engineers and modelling. Yuyal said that he and Nassim were friends and agreed to disagree on certain topics...

Stress testing modelling parameters. Next up was Peirpaolo Montana, Head of Model Validation at West LB. Having joined the finance industry out of a career in mathematics and then at a regulator, Pierpaulo began by saying that back in the heady days of 2004 the banks thought that their own risk management systems and practices were well ahead of the regulators. He said that in light of the crisis this proved not to be the case but he now feels that this is now more evenly balanced (not sure I would agree, still lots of catchin to do for some institutions I would suggest).

He said that whilst regulators require the validation of risk models and pricing models, and that stress testing of a portfolio is required, that the stress testing of a pricing model is not a requirement and has received much less attention and in his view was not done to much degree before 2007. His point here was that pricing models should work under stress too, otherwise they are a weak foundation for building other risk measures such as stressed VAR.

Whilst focussing on pricing models, he mentioned that risk models also need to be carefully chosen and appropriate to the institution and the types of trading activities it undertakes. As an example he put forward that a simple VAR calculator might be appropriate for a long only equity fund but completely innappropriate for a relative value portfolio.

He said that stress testing had recently received much more attention as a risk management tool and cited the BIS document "Revisions to the Basel II market risk framework" where stressed VAR is introduced as part of the regulatory capital charge calculation. He also mentioned that in order to avoid "standard model" treatment of complex securitised products an institution must be able to demonstrate that its VAR model can cope with these products under times of market stress.

Pierpaulo then described the stress testing of base correlation in CDO pricing, and how even moving the base correlation from its usual level of 70% to 99% would not have predicted the valuations observed in the recent crisis. In this way he says that stress testing of models can detect implementation problems and some model weaknesses, but it cannot assist in coping with structural breaks in the market. He also discussed how the B-S-M model is used everywhere (even places it should not really be valid for) since it is a robust model based on the no-arbitrage hypothesis - in contrast the CDO base correlation and other models are not so robust since they are not arbitrage free.

(end of post!)

Posted by Brian Sentance | 18 September 2009 | 4:30 pm

Regulatory moves and moods

Seems that the latest EU and Basel Committee proposals on banking regulation cannot make everyone happy (now there's a surprise...). Whilst many seem very happy at the incremental nature of the proposals to increase capital requirements for securitisations and proprietary trading, some of those in the Glass-Stiegal/banking utility camp are less than impressed. I am with the incremental camp myself, but have to acknowledge that the sceptics are not short of ammunition when saying that we are heading back to the future...meanwhile over in hedge fund land, London is currently in a very bad mood with the EU...

Posted by Brian Sentance | 15 July 2009 | 6:02 pm

Debt hides volatility from Taleb

Nassim Nicholas Taleb and one of his colleagues are back in the FT today with an article on the "evils" of debt and why the only solution to the economic system's woes is (start the fanfare, this is scary stuff!) the "immediate, forcible and systematic conversion of debt to equity". The main points of the article are that:

Debt and leverage lead directly to fragility in the system whereas equity is robust at absorbing extreme variations in the system.
The economic system is experiencing more extreme events (more "Black Swans") than ever before rendering mainstream economic forecasting useless.
Debt hides volatility as a loan does not vary outside of default whereas an equity investment has volatility but its risk is more visible and as a result more manageable.

I think the last point on debt hiding volatility is quite profound - on a personal basis I would put it into the category of one of those things that you know but it becomes clearer when expressed in a different way, usually (in my case!) by somebody else. Its implications are illustrated particularly well in the following extract from the text:

"Thus debt is the province of both the overconfident borrower who underestimates large deviations, and of the investor who wants to be deluded by hiding risks."

The article is dramatic (as is usual with Taleb, see post) and short on detail of how such a fundamental conversion of debt to equity should happen from a practical point of view. It is nonetheless thought-provoking, particular around the use of flawed economic models being used to get us out of a crisis that the underlying maths helped us to get into, and the consequent proposal that we shouldn't try to model and control the risks of the system but instead endorse equity as the defensive, stabilising shock-absorber of choice. Maybe I should call my insurance broker, I think I need to increase my cover...

Posted by Brian Sentance | 15 July 2009 | 5:21 am

Tick Size Harmony...

...in a rare show of co-operation (I wonder what is the carrot or (regulatory) stick here to motivate this?) European exchanges and MTFs seem to have agreed on standardising tick sizes (or at least to have two standards rather than twenty five!). Extract from article on AutomatedTrader:

"From the perspective of each trading venue, strong incentives exist to undercut others in terms of tick sizes, which is not in the interest of market efficiency or the users and end investors. This might, in turn, lead to excessively reduced tick sizes in the market. Excessively granular tick sizes in securities can have a detrimental effect to market depth (i.e. to liquidity). An excessive granularity of tick sizes could lead to significantly increased costs for the many users of each exchange throughout the value chain; and have spillover costs for the derivatives exchanges' clients."

Posted by Brian Sentance | 9 July 2009 | 8:11 am

Das's Dazzling Derivatives

Satyajit Das adds an interesting contribution the debate on OTC derivatives and the drive towards CCP in his article in the FT today (see earlier post for background). The opening paragraph sets the tone:

'US and European Union proposals for over-the-counter derivative regulations are consistent with H.L. Mencken's proposition that "there is always a well-known solution to every human problem - neat, plausible and wrong".'

Main points from the article:

A single CCP would certainly qualify for "too big to fail"
The success of CCP depends on collateral and collateral valuations may underestimate risk and value since these are usually based on historical volatility
Cross-margining exposes the CCP to correlation risks in offset methodologies
CCP depends on valuing contracts that depend upon liquid markets
CCP margining requirements may communicate market stress to more participants and in turn create more stress
Regulators are missing the point with CCP and should look addressing the core issue of innovation and complexity hiding excessive profits in derivatives

As a related aside, probably also worth taking a look at the following article on the return of securitisation.

Posted by Brian Sentance | 8 July 2009 | 2:52 pm

Lessons for Risk Management - Wilmott and Rowe

Great event organised by PRMIA and IAFE last night at Goldman's London offices with a long title:

"A Little Thought Goes A Long Way and Lessons for Risk Management from the Current Crisis".

The event was moderated by Giovanni Bellossi of FGS Capital, and featured speaking slots by Paul Wilmott and David Rowe of Sungard. Here are my notes on the evening, please forgive any innaccuracies, and please persevere through some of the techy quant stuff, as their general points are well worth understanding.

Giovanni quoted from Nassim Taleb about how VAR is invalid and that mainstream financial mathematics should be banned (or words to that effect, see earlier post on Taleb)
He added that whilst what Taleb says cannot be ignored, he said that despite the current crisis and its causes that we should not "throw the baby out with the bathwater" and added that Taleb "...is not only able to recognise a cow but also knows how to milk one."
Giovanni said that financial mathematics has much to offer and that whilst VAR is simply a number, one of its great benefits has to make one measure of risk simple and compelling enough to get traders and risk managers talking.

Paul Wilmott then took the floor and put forward his thoughts:

On Taleb and the Black-Scholes Model

Paul mentioned that he and Taleb were great friends, and whilst he agreed with much of what Taleb says he has areas of disagreement, particularly over the use of the Gaussian distribution in finance and its implications for "fat tail" events
Paul Googled "Taleb" and found more entries for Taleb than for Stephen Hawkin which shows how much attention had come his way due to the "Black Swan" debate
He thinks that he and Taleb are the "Marmite of finance" (for those of you not in the UK who do not know Marmite, it is a sandwich spread that you either love or hate, never anything inbetween)
He suggested that every quant needs a much more fundamental and practically grounded understanding of financial mathematics.
Paul refered to some work (mentioned by Giovanni) that Peter Carr of Bloomberg had done on discrete daily hedging that showed that this option replication technique could remove up to 85% of the risk and that all quants should know about this 15% error term when trying to calculate an option price to the Nth decimal place.
He described how in the past he had set up a volatility arbitrage hedge fund, wanting to improve upon the flawed assumption of the Black-Scholes (B-S) model that volatility is constant and to build the world's best volatility model for option pricing.
Paul said that he did build the world's best volatility model (?!), but soon found it took too long to calculate, so he reverted back to B-S and has become an unfashionable fan of the model and its assumptions.
He added that many of the variants on B-S to overcome its limitations have made the model worse and harder to calibrate.
In some part due to Taleb's opinions on fat tails of distributions, B-S and other models are now very unpopular but Paul claims that not many people have actually bothered to robustly test the B-S model or take a practical, evidence based approach such as that adopted by Peter Carr.
Paul then showed some example charts and said that with a limited number of opportunities for regular time-period hedging it was not valid to use risk-neutral pricing whereas if the same number of hedges could be used optimally (implying at irregular time periods) then risk-neutral was valid and hedging could be more effective. He emphasised that this was the kind of practical stuff that a quant should know and that quants show know less about esoteric complex financial mathematics.

Correlation

Paul said that of all of the issues that need addressing in mathematical finance, the one that he has very few answers on is correlation.
He showed that even basic questions about correlation are poorly understood, even by quants - a question he asks some quants was that if two asset prices both start out at 100, and they have a correlation (of returns) of 1 (perfect correlation) what is the price of the second asset after a year if the first moves to 200. The answer is not 200, and he showed how assets could diverge in overall direction but still have a correlation of 1 or rise together with a perfect negative correlation of -1.
Paul illustrated how correlation was a very blunt measure that is mis-used by people to summarise the highly complex and historically unstable relationships between assets driven for example by industry sector success (leading to +ve correlation) or competitive success (leading to -ve correlation)
As a result, he said that financial products whose value depends on correlation should not be transacted in any great size and moved on to the example of CDOs, where a CDO with 1,000 underlying mortgages has been modelled with 1/2 million correlations all assumed to be 0.6. Why this assumption should be made was his main point.

Sensitivity to Parameters

His main point here was that a constant should not be varied, otherwise it is not a "constant", in particular focussing on volatility used in the B-S model and the calculation of Vega as prices are moving.
Paul added that sensitivity measures may apply locally and is such may look comparible from one situation to another, but quants need to understand how outputs respond over a wider range of inputs, and not to be inhibited by accepted practices and beliefs.

Complexity

Models need to be robust and transparent, and that quants should aim for the mathematical sweet spot.
Paul put forward the following analogy that at least when driving an old car over a long distance, you knew that the car was likely to break down at least once, but you also knew that it was likely that you could fix it. Contrast this with driving a modern sports supercar and finding that it has (unexpectedly?) broken down - you don't know how to fix it, you do not complete your journey and it costs you an ordinate amount of money to put things right...

Self-Referential Feedback

Paul described here how the hedging of derivatives contracts in the underlying markets can cause price movements in underlying markets that cause derivatives contracts to re-price that cause more hedging in the underlying markets...
He was critical of credit derivative pricing as being too complex and too "mathsy" (...but had to admit that he had also endorsed some of this work at the time)

Calibration

Paul said that model parameter calibration is the devil's work...
He refered us to inverse problems in mathematics as a background to this issue in mathematical finance.
He emphasised how markets and price behaviour is fickle and driven by human opinions and behaviours
He said that on-going and regular re-calibration of a model is very, very likely to mean that the model is wrong (he had a particular example of calibrating a particular model he hates where vol is a function of underlying price and time.

David Rowe, Sungard's specialist spokesman on risk management, then took over from Paul and set out his five topics for discussion:

Statistical Entropy - fundamentally that information can only be extracted from data, with the emphasis on extraction of information (from that already in the data) rather than creation of new information.
Structural Imagination - that we need to be aware of how the market assumptions we make are themselves a model and that we need to spend more time on thinking about what could happen outside our current understanding or market experience.
Self-Referential Feedback - the feedback loops in pricing, risk management and economics
Complexity and Dark Risk - when you add (untested) complexity of a model to limited data sets you get a recipe for disaster.
Alternate Means of Valuation - when the primary means of valuing a security is not available (illiquid markets anyone?) then what is the secondary means of calculation value.

Some further notes from David's talk:

AAA rating should imply a failing once every 10,000 years, with some super senior CDO tranches being rated as better than AAA - David pointed out that even as recently as the early 1990s there were problems in the US housing market that indicated that AAA did not mean what it was taken to mean.
On structural imagination, David said that quants and risk managers must look for unrepresented variables in a model and track them early to monitor their effects
On feedback he cited an example where increased returns drove product innovation which drove up (CDO) volumes, which caused underwriting standards to fall, that allowed further complexity, that then led to unreliable risk estimation which then led to more product innovation... and so on.
He suggested that quants adopt the "second means of valuation" mantra in a similar way to credit specialists always having the mantra when assessing credit of "what is the second means of repayment" (e.g. a lien on a house) when the primary means (mortgage payments) goes away.
David showed a nice classification from an IASB paper on classifying financial instruments:

Level 1: fair values measured using quoted prices in active markets for the same instrument.

Level 2: fair values measured using quoted prices in active markets for similar instruments or using other valuation techniques for which all significant inputs are based on observable market data

Level 3: fair values measured using valuation techniques for which any significant input is not based on observable market data

David additional proposed the interesting level of "Level ?" for some products, and said that obviously more attention needs to spent on Level 2 and 3 instruments under conditions of reduced (non-existant?) market liquidity.

Summary Session:

Paul and David then answered some questions from the audience:

Paul said that some risk managers lacked the imagination necessary for good risk management, being confined in standard procedures, beliefs and ways of doing things. He wants risk managers who are good at thinking laterally.
Paul said that risk management was often an afterthought, not part of the trading process.
David said that VAR has proven useful despite its weaknesses, in his opinion preventing failures from non-extreme events regardless of the recent extremes
David said that in answer to Taleb's criticism of using history in modelling, it quite frankly is all we have to go on. He quoted Mark Twain in that:

"History does not repeat itself but it does rhyme"

The talks were interesting, and even on points that have been discussed elsewhere both speakers had some interesting slants and good analogies. But maybe I am biassed, as the wine afterwards wasn't bad either!...

Posted by Brian Sentance | 3 July 2009 | 11:28 am

Best execution 2009 - July 1st 2009

A few summary points I took from the Best Execution Europe 2009 event courtesy of Incisive Media that I attended yesterday morning.

The event started with a presentation by Michael Fridrich, Legal and Policy Affairs Officer of the European Commission:

From what Michael was saying then in my view, it seems that the EU is using the G20 declaration on financial stability in April as a remit to regulate in many areas (not all of which related to the current crisis, see last paragraph in this post)
He said that the EU is currently working on removing national options/discretions with respect to financial markets in order to create a single EU rule book and combining this with stronger powers for supervisors including much harsher sanctions against offending institutions
They are also reviewing the necessary information provided to investors in OTCs, even if the investors qualify as "professional investors" under Mifid.
The EU is currently reviewing Mifid and the Market Abuse Directive (called "MAD" which is at least humorous...)
EU is also unsurprisingly looking at the regulation of Credit Ratings Agencies (CRAs) given their involvement in rating CDOs and other structured products

So in summary it was a civil servant PR exercise with few surprises, other than we are going to regulate anything that moves. On to a panel debate on "build vs. buy" for execution management software. I will try and put my obvious vendor bias to one side in summarising this one:

The panel summarised that this decision was about the usual issues of time to market and what is an institutions core IP
A senior IT manager from JPMorgan said they both build and buy - but given the size of their organisation and the need to innovate they do build a lot
The COO of Majedie Asset Management said that "build" was "20th Century" and the IT should focus now on "assembly"
He added that if IT lead a procurement process he finds this tends to lead to more proprietary solutions than if business is managing it.
He summarised that business people should have the mandate to define inputs/outputs to a requirement and that IT were not qualified to do this.
Putting it more controvertially he suggested that IT people should work for IT companies
The JPMorgan guy responded that "assembly" of external components can lead to excessive staffing in managing all the plumbing, and that build in house could build a more generic and targetted platform that would need less management
The moderator summarised the build vs. buy decision as one of balancing time to market and how bespoke a solution is alongside of looking at the risks for buying of 1) integration risk 2) vendor risk and for building of 1) delivery risk 2) key man risk

The debate on this was pretty standard, but the guy from Majedie was at least controvertial in what he was saying, (including at one point that "investment management does not scale"). I assume he is trading simple products and as such is able to outsource more than the JPMorgan manager. My own slant is that more vendor products need to be designed to integrate easily with the IPR of a financial institution i.e. less black box.

Tom Middleton of Citi then did a presentation on (equity) market liquidity and market fragmentation:

He started by saying the Smart Order Routing (SOR) was like "Putting Humpty-Dumpty back together again" from all the sources of liquidity now available under Mifid.
Being no expert in SOR, I was excited (?) to learn a new term which was "finding Icebergs" - apparently an "Iceberg" is a large non-public ("dark") order being posted with a much smaller public trade order.
He said that market fragmentation will increase further but there will be less trading venues as the market consolidates.
New algorithms will be developed more specifically for trading on dark pools of liquidity
Clearing and settlement costs are still high across Europe which limits the usage of small size orders in trading but trading volumes will continue to grow
The drive to ever-lower latency will also continue
Usage of SOR will grow

Tom's presentation was then followed by a panel debate on Smart Order Routing:

A manager from Baader said that the German area market of Europe was not very sophisticated yet, with most German clients specifying exactly where the trade should be executed hence nullifying the need for SOR.
Deutsche Bank (DB) mentioned that having both US and EU operations had helped them get SOR in place for the EU quicker given their US experience.
UBS and Baader both said that Algo trading and SOR are increasingly integrated and will merge with the Algo define what and how to trade and the SOR component determining where
DB said that a "tipping point" towards usage of SOR in the EU will occur when more than 20% of trading occurs away from the primary exchanges.
DB said that 60% of US liquidity was due to algorithmic trading and that there were now no EU barriers to this happening in European markets and bringing with it increased liquidity, although issues such as not having a consolidated market tape for trading made things more difficult
Neonet said that clearing and settlement costs were still a barrier to widescale SOR adoption.
IGNIS Asset Management said that SOR was a "high touch" service for them, requiring SOR vendors to be very responsive and client focussed. In selecting SOR vendors they were concerned with data privacy and also with having a real-time reporting facility to see how orders were being filled.

And finally (at least before I had to leave) there was a presentation by Richard Semark of UBS on Transaction Cost Analysis (TCA):

He was surprised to find that there were not many presentations around on TCA
TCA vendors are behind the times and are not up to date with current developments
Historically TCA was about what had happened (about 3-4 months ago!)
Mifid has driven fund managers and traders to talk more and TCA is a key part of this conversation
It is hard to look bad against traditional TCA measures such as VWAP if a stock is always rising or always falling, and this can hide a lack of performance and "value add"
Using "Dark" for non-displayed liquidity has been a publicity disaster for the electronic trading industry
Much Smart Order Routing (SOR) is still based on static tables of trading venues that are updated on a monthly or quarterly basis
Market share by volume of a venue is not necessarily correlated with obtaining the best prices in the market
TCA should be based upon a dynamic benchmark that responds to the market and trades done not against a static one
Trade performance is not linear with trade size which is an incorrect assumption in much of TCA
Trade risk (variability in outcomes) deserves more focus
Portfolio TCA is much more complicated where the trading of a single stock cannot be looked at in isolation of its effects on the whole portfolio
Real-Time TCA is becoming ever more important to clients since it allows them to understand more of what is going wrong/right with filling an order
TCA providers are not doing a good job for clients, not using the right data or answering the right questions for clients

Not sure who the TCA providers he refers to are, but maybe I should find out to see what they offer...

Posted by Brian Sentance | 2 July 2009 | 8:17 am

Over The Counter Arguments

George Soros has waded back into the current saga concerning OTC derivatives in his article last week in the FT. The main part of the article focusses on financial markets reform, but ends with a vehement attack on derivatives, building upon some of his earlier ideas (see post) and seemingly going much further:

"Finally, I have strong views on the regulation of derivatives. The prevailing opinion is that they ought to be traded on regulated exchanges. That is not enough. The issuance and trading of derivatives ought to be as strictly regulated as stocks. Regulators ought to insist that derivatives be homogenous, standardised and transparent."

He ends by saying that "CDS are instruments of destruction that ought to be outlawed.". To the extent that Mr Soros attracts press/political attention is probably something the OTC markets should worry about, although it would seem his views are already consistent with many involved in influencing the US financial markets policy - take for instance the submission by Christopher Whalen to the US Senate on OTC Derivatives:

"Simply stated, the supra-normal returns paid to the dealers in the closed OTC derivatives market are effectively a tax on other market participants, especially investors who trade on open, public exchanges and markets."

Fortunately however there are also some more balanced views around - I found the following post on the "(in)efficient frontiers" blog, which references the earlier Senate submission by Richard Bookstaber on OTCs. Mr Bookstaber starts by saying that derivatives can improve financial markets, allowing investors to shape returns, exactly meet contingencies and package risk. Mr Bookstaber also puts forward a very clear summary how participants have also over recent years use derivatives to game the system to achieve tax avoidance, investment mandate avoidance, speculation and to hide risk-taking.

So back to the Soros article, there was a letter in response a few days later from a partner at the legal firm Ashurst's, saying that unfortunately risk does not confirm to a standard. In this I agree, standardising contracts can lead to increased complexity - there was a recent example given by a swaps dealer at JPMorgan who said that a corporate with particular cashflows to be hedged does want to be dealing with the basis risk and admin of using standardised contracts - the corporate treasurer wants something that matches the exposure they have and takes it away, end of story. Again this is an example of derivatives "risk" not being just about the product type, but also about which institution is holding the contract and what they are using it for (see earlier post).

Not sure however how much the Ashurst's partner who wrote the response letter is worried about lucrative legal fees for OTC derivative contracts dying off if Soros-like standardisation occurs - it is a world of vested interests at the moment, never more vested than in a crisis...

Posted by Brian Sentance | 2 July 2009 | 7:26 am

Risk in the Hands of the Holder?

Given the ongoing debate about "too big to fail" and whether we should head back to the days of the Glass-Steagal Act, then here is a slightly different slant on the problem of systematic risk put forward in an article by Avinash D. Persaud.

In the article, Avinash makes the very good point that increasing capital requirements across the board is not the only response that regulators should consider, and that the risk of a financial product cannot be determined in isolation of who is holding it:

"At the heart of modern regulation is the erroneous view that risk is a quantifiable property of an asset. But risk isn't singular. There are credit, liquidity, and market risks, for instance—and different parts of the financial system have different capacities to hedge each. Thus, risk has as much to do with who is holding an asset as with what that asset is. The notion—popular in the U.S. Congress—that there are "safe" instruments to be promoted and "risky" ones to be banned is deceptive."

Obviously the last point is very relevant to the OTC markets at the moment. Avinash suggests that capital requirements should be tailored to what type of organisation is holding a risk and that organisations ability to hedge it, and outlines past mistakes made by regulators:

"By requiring banks to set aside more capital for credit risks than nonbanks must, regulators unintentionally encouraged banks to shift their credit risks to those who wanted the extra yield but had limited ability to hedge this type of risk. By not requiring banks to put aside capital for maturity mismatches, they encouraged banks to take on liquidity risks they couldn't offset. Moreover, by supporting mark-to-market asset valuations (which make institutions value holdings at their current price) and short-term solvency requirements, regulators discouraged insurers and pension funds from taking the very liquidity risks they are best suited for."

On banks and credit risk, then for those interested there is a good regulatory arbitrage example for credit risk described in the following article. Fundamentally I think the paragraph above illustrates some of the reasons why it is right to worry about rushing in new regulation too quickly - certainly things need to change but when dealing with large and complex systems (i.e. in this case Financial Markets) changes should be introduced incrementally in order to understand how the system responds.

Given the political imperative to "do something" then regulators find it all too tempting to stick their noses in everywhere, even in areas that did not lead us to the current crisis - take for instance the regulatory initiatives over the past year in short selling, hedge fund regulation and more recently the dangers of "dark pools" (at least dark pools sound scary I guess?). Where will the next "bogey man" appear on the regulator's radar and what will be the unintended consequences of government pressure on regulators to keep us all "safe"?

Posted by Brian Sentance | 2 July 2009 | 6:00 am

Risk as Sales Support?

Article in FTFM yesterday saying that the risk function is being ignored by asset managers when formulating new financial products.This seems consistent with some recent comments from one risk manager who said that their role was a lot to do with sales support i.e. to convince potential investors that the asset manager has good risk management capability. Given all the discussions on the sell side about the role of risk managers and the risk function, sounds like the debate should open out more onto the buy-side too.

Posted by Brian Sentance | 30 June 2009 | 6:00 am

Data Pirates and Getting a Share of the Booty

Seems like data piracy (illegal sharing of logon IDs and scraping data) is costing the financial information industry around $8 billion in subscription revenue each year reports Inside Market Data. My first reaction is that $8 billion is a lot to loose, and shows just how (surprisingly?) big the whole market is ($23 billion apparently). My second is that I wonder how many end-users who share logins illegally would not that if they faced the full costs, so maybe the number should be a lot less? Either way the stat is interesting, particularly at a time when Bloomberg seems (!) to be taking a more constructive stance on data provision and partnering. Ironic also that the report suggests that the biggest set of guilty parties on illegal page scraping are the data vendors themselves, checking on each others data.

The company that put the survey together, Burton-Taylor, seem to have some interesting background on the major data vendors. The first is on news content, saying that Bloomberg seems to concentrate on news alerts whereas Reuters seems to put more emphasis on news analysis. The second shows shows financial information/analysis revenue broken down by vendor and geography in 2008, showing how dominant Thomson Reuters and Bloomberg are in the US and EMEA, with Quick having significant share with the big two in Asia. The third shows revenue broken down by segment and geography with FX/Fixed Income Sales & Trading, Equity Sales & Trading, Investment Management and Corporate expenditure dominating.

Posted by Brian Sentance | 29 June 2009 | 7:55 am

Twittering the Wisdom of Crowds

Deserving an award for title alliteration, an article on Finextra has announced that Streambase Systems have connected their system to Twitter, the fashionable microblogging site. Regardless of the intent, it is an excellent marketing exercise by Streambase (er, maybe one that I should remember for the future!...).

Reasonable comments from Finextra at the end of the article, saying that Twitter is a notoriously bad source of information, very open to (designed for?) rumour, and as such it would be difficult to see what real information traders could extract from the noise. At one level, then rumour and counter-rumour are the basis of markets, although the recent financial crisis has illustrated how powerful rumours can be. I would suggest it begs the question as to when rumour and counter-rumour is part of the price formation process, and when it becomes market manipulation.

On a related note, the Efficient Market Hypothesis (EMH), the financial theory that all information (including rumours) is reflected in current prices, has been coming under some attack in the press recently. With a fund-management and Monty-Pythonesque slant, James Montier of Société Générale takes EMH to task in his recent article in the FT (see Pablo Triana for an alternative view).

My opinion is that EMH has still got some legs in it as a model, but behavioural finance probably has a lot more to explain (or rationalise?) about this theory and others in light of recent events. Anyone got a different opinion, or do I need to open a Twitter account to find out?...

Posted by Brian Sentance | 25 June 2009 | 10:16 pm

Liquidity Risk

Our think-tank friends at JWG-IT organised a great event yesterday, with several of the top banks coming together to share their thoughts on what is currently causing them the most pain in implementing the FSA liquidity risk requirements (see FSA Consultative Paper CP08/22 for background).

A few points I took from the meeting:

FSA is moving from a "principles" based approach to regulation to "outcomes" on to "proof of judgement" as the basis for assessing financial institutions
What liquidity stress tests the FSA wants the financial institutions to perform is still far from clear
The above uncertainty is not helping when combined with an implementation deadline of this October
Whether liquidity risk must be managed at the branch or group level is a key unanswered question which has enormous implementation implications
The data requirements are enormous and since a group-wide issue requirements greater central access to data across all departments - unlike traditional market risk which is currently more siloed within each business division
The granularity of data required (down to transactions, detailed cashflows for complex derivatives) is very challenging
Management of intraday liquidity requires real-time cash transaction reporting which is currently not being done/is difficult to do
"Ownership" of liquidity risk implementation typically resides within a bank's treasury function but awareness, ownership and involvement of all departments (e.g. market risk) could be greatly improved

A lot more interesting issues and detail on this meeting plus survey results will be available from JWG-IT soon (see their liquidity risk site)

Posted by Brian Sentance | 22 May 2009 | 2:41 pm

Liquidity Derivatives - the next OTC?

Given the drive the FSA is making in forcing financial institutions to implement "Liquidity Risk Management" (see background on JWG-IT site) are we going to see renewed interest in the creation of "Liquidity Derivatives" to hedge liquidity risk? I found the following post on the subject applied to hedge funds but not much information else where, although Tony Jackson did an interesting article on liquidity in the FT last week, indicating that liquidity derivatives have been tried before with little success.

I was thinking of the advent of credit derivatives being driven in no small part by Basel II regulation on capital charges for credit risk. Maybe given the current battle going on around OTC regulation (see FT feature today) there are institutions working on liquidity derivatives but nobody in the finance industry wants to admit that they are already creating the next "innovative" OTC to nullify regulatory charges?

Mr Geithner better watch out, innovation will always beat "rules" in my view...

Posted by Brian Sentance | 21 May 2009 | 6:20 am

Alternatives Need a Bigger Umbrella?

Interesting article in the FT today about why the US exodus from traditional exchanges might not be repeated here in Europe, which is contrary to the recent marketing mantra of the alternative trading venues such as Chi-X, Turquoise and Equiduct. If correct, the economics outlined in the article look justifiably prohibitive:

"Merely to break even, an alternative platform with a cost base of about €10m would need to do 100m trades a year. Quite a task, given that the 208-year-old London Stock Exchange, which reports full-year figures on Wednesday, said in March it was on course for about 190m in its UK orderbook."

The article points out the difficulty of starting an alternative trading venue against a dire economic background and emphasises this by ending with:

“Xavier Rolet, the LSE’s new chief executive, should be praying for rain.”

Posted by Brian Sentance | 19 May 2009 | 1:16 pm

Regulating OTCs Out Using Capital?

Following on from the warnings on over-regulation in my post last week on the OTC markets in London, Larry Tabb of the analyst firm the Tabb Group is pointing towards increased capital requirements as the stick the regulators will use to move the finance industry away from the perceived dangers of the OTC markets (see article here).

Posted by Brian Sentance | 8 May 2009 | 4:58 pm

Technical and Human Analysis

The FT Alphaville Blog put up a post earlier this week about Bloomberg being critical (see article) of technical analysis and its ability to make money using techniques such as "Bollinger Bands". In summary Bloomberg have backtested some of the most common technical analysis strategies over recent years and found the majority of them have lost money.

This took me back a few years to a conversation with a derivatives trader in Hong Kong who having come from a mathematical background was a natural believer in "efficient markets" in that all information known about an asset was reflected in its current market price (and hence that drawing some lines on a chart would not tell you anything that the market had not already factored into the price of the asset).

On one occaision, the trader was phoned by a broker who asked him if he had "seen that the Hang Seng had just broken its resistance point". Initially having dismissed the broker's call as rubbish, then upon reflection he knew the broker was going to be calling many of the major players in the market and that many would listen to the broker and act upon it. Thinking about it further, the trader decided to go along with the broker's trading idea simply because others would and it would become a self-fulfilling opportunity. Sure enough, the Hang Seng did rise that day as the broker had predicted and the trader made some (reasonable) money from the trade.

So whilst technical analysis has its failings, human behaviour ("herd instinct" or the ubiquitous "Mr Market") cannot be ignored. Given the underlying human causes to the current crisis, the more work that can be done on better modelling of human behaviour is all to the good in my view.

Posted by Brian Sentance | 7 May 2009 | 11:37 am

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