Truly "Open" Bloomberg?

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

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

Shipping Fair Value...

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

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.

High Frequency Trading vs Flash Trading

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?

Pricing Model Validation: Mitigating Model Risk

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(end of post!)
 

 

Heavyweight Data Management...

...I am very concerned that I have previously missed an important requirement for data management solutions - a heavweight one judging by this great discussion on one of the Microsoft forums.

Regulatory moves and moods

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

Debt hides volatility from Taleb

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

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

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

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

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

Tick Size Harmony...

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

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

Das's Dazzling Derivatives

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

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

Main points from the article:

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

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

Lessons for Risk Management - Wilmott and Rowe

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

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

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

• Giovanni quoted from Nassim Taleb about how VAR is invalid and that mainstream financial mathematics should be banned (or words to that effect, see earlier post on Taleb)

• He added that whilst what Taleb says cannot be ignored, he said that despite the current crisis and its causes that we should not "throw the baby out with the bathwater" and added that Taleb "...is not only able to recognise a cow but also knows how to milk one."

• Giovanni said that financial mathematics has much to offer and that whilst VAR is simply a number, one of its great benefits has to make one measure of risk simple and compelling enough to get traders and risk managers talking.

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

On Taleb and the Black-Scholes Model

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

• Paul then showed some example charts and said that with a limited number of opportunities for regular time-period hedging it was not valid to use risk-neutral pricing whereas if the same number of hedges could be used optimally (implying at irregular time periods) then risk-neutral was valid and hedging could be more effective. He emphasised that this was the kind of practical stuff that a quant should know and that quants show know less about esoteric complex financial mathematics.

Correlation

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

Sensitivity to Parameters

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

Complexity

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

Self-Referential Feedback

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

Calibration

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

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

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

Some further notes from David's talk:

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

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

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

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

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

Summary Session:

Paul and David then answered some questions from the audience:

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

"History does not repeat itself but it does rhyme"

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

Best execution 2009 - July 1st 2009

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

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

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

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

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

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

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

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

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

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

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

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

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

 

 

 

 

Over The Counter Arguments

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

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

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

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

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

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

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

 

Risk in the Hands of the Holder?

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

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

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

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

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

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

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

Risk as Sales Support?

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

Data Pirates and Getting a Share of the Booty

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

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

Which email have you hidden behind?

Pet subject (partly because I have been guilty of it), but good reference article by Luke Johnson of the FT on email and how many of us hide behind it rather than speak face to face to colleagues and clients.

Twittering the Wisdom of Crowds

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

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

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

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

Liquidity Risk

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

A few points I took from the meeting:

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

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

Liquidity Derivatives - the next OTC?

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

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

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

OTC Valuation by SGSS

Given all the recent attention that OTC derivatives have received (see Geithner letter), then a topical update on the work we have done with Societe Generale Security Services (SGSS) on OTC and structured product valuation services has been written up on Securities Industry News. The work involved extensive integration with Mysis Summit, where our TimeScape data and analytics management system is used to provide "Golden Copy" of market, reference and derived data for the derivative products being valued. The section on TimeScape says:

"The Summit FT solutions are integrated with SGSS' market data software tool TimeScape, licensed from London's Xenomorph in November 2007. This produces a "golden copy" of end-of-day prices from 15 different information suppliers. The unit also processes information related to 70 different currencies and 5,000 volatility surfaces, which give three-dimensional views of how much and fast a security can move up or down. With Summit's product, each surface can include between 200 and 500 data points."

From talking to some of the SGSS team at our recent user group, the thing they most seem to value about TimeScape is its ease of use in describing and managing any kind of product, allowing product and market data specialists to use and customise the system without the need for specialist technology knowledge. This echos some of the things that were said about TimeScape after a demo to Lab49 last year. 

Alternatives Need a Bigger Umbrella?

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

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

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

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

Microsoft CEP Surfaces as "Orinoco"

Seems like Microsoft have now gone public on the Microsoft TechEd site that they have a Complex Event Processing (CEP) engine that will be coming to market shortly (see MagmaSystems blog post ). One of my colleagues Mark Woodgate attended a briefing event at Microsoft for this technology back in February this year - here's an extract from some internal notes that Mark made back then:

"Microsoft CEP is very similar to StreamBase conceptually (and not unsurprisingly), in the sense that there are adapters and streams and how you merge and split them via some kind of query language is the same. However, StreamBase uses the StreamSQL which as we have seen is SQL-like in syntax but Microsoft CEP uses LINQ and .NET and although conceptually it is doing the same thing, it does not look the same. StreamBase’s argument was you can be an SQL programmer to use it and don’t need lower-level like .NET; however, it’s not SQL really as it has all these ‘extensions’ you have to learn so using .NET might look more tricky but in fact it makes sense. They don’t have a sexy GUI yet for designing CEP applications like StreamBase but it will be done in Visual Studio 2008.

 

Currently, you build various assemblies (I/O adapters, queries and functions) and then bolt them all together, called ‘binding’ by command line tool. You then deploy the application onto one or more machines using another tool so it’s a manual process right now. They are aware this needs to be made easier and more visual. They are allowing other libraries to be bolted in via the various SDKs so it’s pretty open and flexible. It works well with HPC and clusters/grids (or so they say) and of course can be used with SQL Server. The CEP engine also has a web interface based on SOAP so at least non-Windows based systems can talk to it"

 

The release of this technology will be an interesting addition to the CEP market and to the Microsoft technology stack in general. Assuming performance is at credible levels (i.e. not necessarily leading but not appalling either) it will certainly bring both technical and commercial pressure to bare on existing CEP vendors (see earlier post on Aleri/Coral8) and has the potential to broaden the usage of CEP. Obviously Linux-Lovers (sorry, I didn't mean to be personal...) will not agree with this, but Microsoft is putting together an interesting stack of technology when you see this CEP engine, Microsoft HPC and Microsoft Velocity coming together under .NET.

 

Regulating OTCs Out Using Capital?

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

Data Quality and the Future of Risk

A new survey from the Economist Intelligence Unit (sponsored by SAS) of over 300 financial institutions world-side has put data quality and availability as a key issue to be resolved if risk management is to be fit for purpose following the financial crisis:

"Culture, expertise and data are weak points in current risk management"

A summary of the survey report is available here.