Posts categorized "Spreadsheets"

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

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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:

1. 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?)
2. Traders/Portfolio Managers are the people using the system and implementing it, not the technical staff.
3. 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

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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

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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

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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

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"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

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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

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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

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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

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