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...

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.

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.

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? 

Sybase have acquired Aleri according to Finextra. It was less than a year ago when the complex event processing (“CEP”) vendors Aleri and Coral8 announced their merger (see press release); there was also a big buzz when Sybase announced a CEP capability based on Coral8 and Streambase decided to offer an Amnesty Program for Aleri-Coral8 Customers (see earlier post 'Merging in public is difficult...). And only a few months later, Microsoft announced that their CEP Orinoco (now integrated with SQL Server 2008 as StreamInsight) was heading to market (see post 'Microsoft CEP surfaces as 'Orinoco').

Another sign that CEP is moving more mainstream and that real-time everything is becoming more important? Or a good market for acquisitions?

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.

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?...

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.

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!

I attended the Quant Invest 2009 event for the first time last week in Paris. The event is unsurprisingly about quantitative investment strategies, but with an institutional asset manager and hedge fund focus - so not so much about ultra-high frequency trading (although some present) but more about using quantitative techniques to manage medium/longer-term investment decisions and applied portfolio theory. A few highlights below that I found interesting:

• Pierre Guilleman of Swiss Life Asset Management gave an interesting 1/2 day workshop entitled "A random walk through models":
• He is a strong supporter of the need to understand more about the data and statistical assumptions upon which any quant investment model is based and how these fit with the desired investment objectives (similar to the Modeler's Manifesto)
• He made the point that good models can sometimes be almost annoyingly simple, and cited the example by a Professor Fair of Yale who had determined that US elections were predictable based on simple parameters such as past results, inflation and gdp and that policy did not seem to be a key factor at all - annoying for the politicians anyway! 
• Pierre seems very concerned that the Solvency II regulation applied to Life Institutions will negatively influence the investment policies of many institutions - applying sell-side risk measures like VAR to the insurance industry will drive a more short-term approach to investment. He strongly believes that VAR applied to his industry should have an expected return parameter introduced to fit with longer term investment horizons of 10 to 25 years.
• Bob Litterman of Goldman Sachs Asset Management opened the first "official" day of the conference:
• Bob put forward his "scientific" approach to investment modelling going through the stages of hypothesis, test and implement. He warned against overconfidence in investment (apparently 70% of us think we are "above average"...) and impulsiveness (quick impulsiveness test: "if a bat costs $1 more than the ball, and the bat and ball together cost $1.10 then how much does the bat cost?...") 
• He said that the failure of quantitative investment models in 2007 needed to be understood given the success of quant models over past decades. In particular he thought that quant investment became the "crowded trade" of 2007 with every hedge fund having a quant investment strategy. In terms of why this became a "crowded trade" Bob thinks that the barriers to entry into quant investment (particularly technology) have lowered significantly recently.  
• He noted that factor-based investment opportunities decay quicker than they used to due to increased competition - implying the need for a more dynamic and opportunistic investment approach.  
• GSAM are now looking at new markets and new investment instruments, trying to find areas of market disruption but without following what others are doing in the market.  
• He pointed out the conflict between investors wanting more transparency over what is done for them, against the need to be more proprietary about the investment models developed.  
• Next there was a talk on regulation from the French regulator that was dull, dull, dull both in terms of content and presentation style (when will regulators actually prepare well for the talks they give?)
• Panel debate was also pretty average, with the word "alpha" being used too much in my view - asset managers of a certain type seem to hide behind this word as an opaque "magic wand" to justify what they do.
• Jean-Phillippe Bouchard of Capital Fund Management did a great talk called "Why do Prices Move?". Some points from the talk:
• He started off with a reminder about the Efficient Markets Hypothesis (EMH) and how it says that crashes and market movements are caused by events outside (endogenous to) the market such as news, events etc.
• He then said this was not born out in the data, where extreme jumps in prices were only related to news only 5% of the time.
• Volatility looks like a long memory process with clustering of vol over time - similar to behaviour in complex systems
• The sign of order flow is predictable but the price movement is not, with only 1% of daily order volume accounting for price movements over 5%
• Even very liquid stocks have low immediate liquidity, meaning that price movements can play out over many hours and days as liquidity is sought to "play-out" some change in fundamental price levels.
• Joseph Masri of the Canadian Pension Plan Investment Board then did a good talk on Risk Management:
• Jo said that sell-side risk was easier to deal with in some ways since it involved fewer strategies in high volumes, and hence could be better resourced.
• Buy-side quantitative risk was harder due to its reliance onsell-side research and risk tools, the outsourcing of credit assessment to the credit rating agencies, the loss of Bear and Lehman's having caused the buy-side to have to do more risk management itself (and through third parties) rather than rely on the sell side risk management tools.
• He said that sell-side risk models are a good start for an asset manager, but need to be adapted to give both absolute and relative risk (to a benchmark fund for instance). All models are no substitute for risk governance.
• He described the cross over from risk methods: VAR, stress testing, factor-based and their applicability to market risk, credit and counterparty risk.
• Like Pierre he was not a fan of 1 or 10 day trading VAR being applied to investment managers since this risk measure was not suitable for long term investment in his view.
• On stress testing he said this needed to be top down (using historical events etc) as well as bottom up from knowing the detail of strategy/portfolio.
• In terms of challenges in risk management he said that VAR needed more stress testing to cope with the fat tails effect in markets, that liquidity risk both of counterparties and of illiquid products was vital and the importance of stress testing (he mentioned reverse stress testing) plus also the feedback (crowding effects) of having similar investment strategies to others in the market.
• Dale Gray of the IMF gave a very interesting talk on how he and Bob Merton have been applying the contingent claims model of a company (looking at equity in terms of option payoffs for shareholders and bondholders) to whole economies:
• He said that some of his work was being applied to produce a model for the pricing of the implicit guarantees offered by governments to banks
• He said these models were also applicable to macro-prudential risk
• Very interesting talk, and if he really has something of macro-level risk then this is great relative to the wooly approach by the regulators so far

There were some other good talks from Danielle Bernardi on Behavioural Finance, Martin Martens on Fixed Income Quant Investment, Vassilios Papathanakos on Stochastic Portfolio Theory (seemed to be a "holy grail" of investment model, giving good returns even in the crisis - begs the question why he is telling everyone about it?), Claudio Albanese on unified derivative pricing/calibration across all markets (again another "holy grail" worth more investigation) and Terry Lyons on speeding up monte carlo simulations.

Overall a good conference although the quality of the asset managers present seemed very digital from those who really seemed to know what they talking about to those who plainly did not (in my limited view!). Along this line of thought, I think it be good to test whether there is an inverse relationship between the quality of the asset manager and the amount of times they use the word "alpha" to explain what they are doing...

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!...

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.

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!)
 

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?

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.

Economist Tim Worstall has an distinction to make on the differences between high frequency trading and flash trading in a recent article.

Essentially it is the difference between getting your orders in quicker than every one else, and having a peek at what everyone else is doing before putting your money down.  The SEC appears to be conflating the two and has concerns.

With the world condition in banking, could we see some poorly thought out legislation rushed through so that regulators can be seen "doing something"?  Or would it level the playing field a little so that those trading operations that cannot afford the overhead of super fast computers and networks are not excluded?

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...

...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."

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