CVA - a business driver for breaking down asset silos

Xenomorph's analytics partner Numerix sponsored a PRMIA event at New York's Harvard Club this week on Credit Valuation Adjustment (CVA). The event also involved Microsoft, with a surprisingly relevant contribution to the evening on CVA and "Big Data" (I still don't feel comfortable losing the quotes yet, maybe soon...). Credit Valuation Adjustment seems to be the hot topic in risk management and pricing at the moment, with Numerix's competitor Quantifi having held another PRMIA event on CVA only a few months back. 

The event started with an introduction to CVA from Aletta Ely of JP Morgan Chase. Aletta started by defining CVA as the market value of counterparty credit risk. I am new to CVA as a topic, and my own experience on any kind of adjustment in valuation for instrument was back at JP Morgan in the mid-90s (those of you under 30 are allowed to start yawning at this point...). We used to maintain separate risk-free curves (what are they now?) and counterparty spread curves, which would be combined to discount the cashflows in the model.

Whilst such an adjustment could be calibrated to come up with an adjusted valuation which would be better than having no counterparty risk modelled at all, it seems one of the key aspects of how CVA differs is that a credit valuation adjustement needs to be done in the context of the whole portfolio of exposures to the counterparty, and not in isolation instrument by instrument. The fact that a trader in equity derivatives was long exposure to a counterparty cannot be looked at in isolation from a short exposure to a portfolio of swaps with the same counterparty on the fixed income desk.

Put another way, CVA only has context if we stand to lose money if our counterparty defaults, and so an aggregated approach is needed to calculate the size of the positive exposures to the counterparty over the lifetime of the portfolio. Also, given this one sided payoff aspect of the CVA calculation, then instrument types such as vanilla interest rate swaps suddenly move from being relatively simple instrument that can be priced off a single curve to instruments that needed optionality to be modelled for the purposes of CVA.

So why has CVA become such a hot topic at the banks? Prior to the 2008/2009 crisis CVA was already around (credit risk has existed for a long time I guess, regardless of whether you regulate or report to it), but given that bank credit spreads were at that time consistently low and stable then CVA had minimal effects on valuations and P&L. Obviously with the advent of Lehmans then this changed, and CVA has been pushed into prominence since it has directly affected P&L in a significant manner for many institutions (for example see these FT articles on Citi and JPMorgan)

A key and I think positive point for the whole industry is the CVA requires a completely multi-asset view, and given regulatory focus on CVA and capital adequacy then as a result it will drive banks away from a siloed approach to data and valuation management. If capital is scarcer and more costly, then banks will invest in understanding both their aggregate CVA and the incremental contribution to CVA of a new trade in the context of all exposures to the counterparty. Looking at incremental CVA, then you can also see that this also drives investment into real or near-realtime CVA calculation, which brings me on to the next talks of the evening by Numerix on CVA calculation methods and a surprisingly good presentation on CVA and "Big Data" from David Cox of Microsoft.

Denny Yu of Numerix did a good job of explaining some of the methods of calculating CVA, and in addition to being cross asset and all the implications that requires for having the ability to price anything, CVA is both data and computationally expensive. It requires both simulation of the scenarios for the default of counterparties through time, but also the valuation of cross-asset portfolios at different points in time. Denny mentioned techniques such as American Monte-Carlo to reduce the computation needed through using the same simulation paths for both default scenarios and valuation.

So on to Microsoft. I have seen some appalling presentations on "Big Data" recently, mainly from the larger software and hardware companies try to jump on the marketing band wagon (main marketing premise: the data problems you have are "Big"...enough said I hope). Surprisingly, David Cox of Microsoft gave a very good presentation around the computation challenges of CVA, and how technologies such as Hadoop take the computational power closer to the data that needs acting on, bringing the analytics and data together. (As an aside, his presentation was notably "Metro" GUI in style, something that seems to work well for PowerPoint where the slide is very visual and it puts more emphasis on the speak to overlay the information). David was obviously keen to talk up some of the cloud technology that Microsoft is currently pushing, but he knew the CVA business topic well and did a good job of telling a good story around CVA, "Big Data" and Cloud technologies. Fundamentally, his pitch was for banks and other institutions to become "Analytic Enterprises" with a common, scaleable and flexible infrastructure for data management and analysis. 

In summary it was a great event - the Harvard Club is always worth a visit (bars and grandiose portraits as expected but also barber shop in the basement and squash courts in the loft!), the wine afterwards was tolerably good and the speakers were informative without over-selling their products or company. Quick thank you to Henry Hu of IBM for transportation on the night, and thanks also to Henry for sending through this link to a great introductory paper on CVA and credit risk from King's College London. Whilst the title of the King's paper is a bit long and scary, it takes the form of dialogue between a new employee and a CVA expert, and as such is very readable with lots of background links.

 

 

 

NoSQL - the benefit of being specific

NoSQL is an unfortunate name in my view for the loose family of non-relational database technologies associated with "Big Data". NotRelational might be a better description (catchy eh? thought not...) , but either way I don't like the negatives in both of these titles, due to aestetics and in this case because it could be taken to imply that these technologies are critical of SQL and relational technology that we have all been using for years. For those of you who are relatively new to NoSQL (which is most of us), then this link contains a great introduction. Also, if you can put up with a slightly annoying reporter, then the CloudEra CEO is worth a listen to on YouTube.

In my view NoSQL databases are complementary to relational technology, and as many have said relational tech and tabular data are not going away any time soon. Ironically, some of the NoSQL technologies need more standardised query languages to gain wider acceptance, and there will be no guessing which existing query language will be used for ideas in putting these new languages together (at this point as an example I will now say SPARQL, not that should be taken to mean that I know a lot about this, but that has never stopped me before...)

Going back into the distant history of Xenomorph and our XDB database technology, then when we started in 1995 the fact that we then used a proprietary database technology was sometimes a mixed blessing on sales. The XDB database technology we had at the time was based around answering a specific question, which was "give me all of the history for this attribute of this instrument as quickly as possible".

The risk managers and traders loved the performance aspects of our object/time series database - I remember one client with a historical VaR calc that we got running in around 30 minutes on laptop PC that was taking 12 hours in an RDBMS on a (then quite meaty) Sun Sparc box. It was a great example how specific database technology designed for specific problems could offer performance that was not possible from more generic relational technology. The use of database for these problems was never intended as a replacement for relational databases dealing with relational-type "set-based" problems tough, it was complementary technology designed for very specific problem sets.

The technologists were much more reserved, some were more accepting and knew of products such as FAME around then, but some were sceptical over the use of non-standard DBMS tech. Looking back, I think this attitude was in part due to either a desire to build their own vector/time series store, but also understandably (but incorrectly) they were concerned that our proprietary database would be require specialist database admin skills. Not that the mainstream RDBMS systems were expensive or specialist to maintain then (Oracle DBA anyone?), but many proprietary database systems with proprietary languages can require expensive and on-going specialist consultant support even today.

The feedback from our clients and sales prospects that our database performance was liked, but the proprietary database admin aspects were sometimes a sales objection caused us to take a look at hosting some of our vector database structures in Microsoft SQL Server. A long time back we had already implemented a layer within our analytics and data management system where we could replace our XDB database with other databases, most notably FAME. You can see a simple overview of the architecture in the diagram below, where other non-XDB databases (and datafeeds) can "plugged in" to our TimeScape system without affecting the APIs or indeed the object data model being used by the client:

Data Unification Layer

Using this layer, we then worked with the Microsoft UK SQL team to implement/host some of our vector database structures inside of Microsoft SQL Server. As a result, we ended up with a database engine that maintained the performance aspects of our proprietary database, but offered clients a standards-based DBMS for maintaining and managing the database. This is going back a few years, but we tested this database at Microsoft with a 12TB database (since this was then the largest disk they had available), but still this contained 500 billion tick data records which even today could be considered "Big" (if indeed I fully understand "Big" these days?). So you can see some of the technical effort we put into getting non-mainstream database technology to be more acceptable to an audience adopting a "SQL is everything" mantra.

Fast forward to 2012, and the explosion of interest in "Big Data" (I guess I should drop the quotes soon?) and in NoSQL databases. It finally seems that due to the usage of these technologies on internet data problems that no relational database could address, the technology community seem to have much more willingness to accept non-RDBMS technology where the problem being addressed warrants it - I guess for me and Xenomorph it has been a long (and mostly enjoyable) journey from 1995 to 2012 and it is great to see a more open-minded approach being taken towards database technology and the recognition of the benefits of specfic databases for (some) specific problems. Hopefully some good news on TimeScape and NoSQL technologies to follow in coming months - this is an exciting time to be involved in analytics and data management in financial markets and this tech couldn't come a moment too soon given the new reporting requirements being requested by regulators.

 

 

 

Risk models and tools at Baruch College

Emanuel Derman gave the last presentation of the day on mathematical models and their role in financial markets. His presentation seemed to build on some of his earlier ideas with Paul Wilmott on the "Modeller's Manifesto".

Emanuel said that there was a "scandal based on models" is wrong; models did (and do) have their faults but they were not a root cause of the crisis. He started his presentation (somewhat "tongue in cheek") by putting forward a "Theory of Deliciousness" to see how one might arrived at the value of something being more or less delicious. This involved discussion of "realised deliciousness" and "expected or implied deliciousness", plus definitions around equally (relatively) delicious things and absolute deliciousness. See post on FT Alphaville for more background, but fundamentally by analogy Emanuel was putting across that there is no "fundamental theory of finance" and that finance is not physics.

He said that economists do not know the difference between theorems and laws. He seemed to be critical of some recent work from Andrew Lo (see recent post) on putting together a "Complete Theory of Human Behaviour" for once again attempting to codify something that it is uncodifiable.

Emanuel described how economists should be more aware of what is and isn't a:

• Metaphor - using something physical/tangible to represent a less tangible concept or idea. See this link for his interesting example on sleep/life and debt interest
• Model - extending the behaviour of one thing to another. A model aircraft is a very useful model of a full-size aircraft with know inputs and useful outputs of interest. We can try to model the weather but here the inputs are known (temperature, wind etc) but the model is hard to define. In finance it is hard to really see what both the inputs are and what the outputs are too.
• Theory - the ultimate non-metaphor. Here he gave the example of Moses asking the burning bush who shall I say sent me to which God replies "I am what I am". Put another way, you can't ask why on a theory, it just is.
• Intuition - a premise put forward based neither on logical progression nor on experimentation.

Emanuel said that in Finance there is no absolute value theory, and the majority of models are relative value in nature. From a common sense point of view, the world is not a model. Things change dynamically and in this way effectively all models are wrong to some degree. In summary all financial models are short volatility.

He ended his presentation by saying that nature cares more about principles than regulations (prescriptive regulators beware I guess). His parting quote was by Edward Lucas who said "If you believe that capitalism is a system in which money matters more than freedom, you are doomed when people who don’t believe in freedom attack using money."

Panel Debate

Some highlights:

• Bruno Dupire of Bloomberg said that it was important that a financial product was aligned with the needs of the customer, and cited certain complex products (with triggers) as being more in the interests of the vendor not the customer.
• Bruno also said that the hedgeability of a product was also key to a more stable financial system (presumably pointing at products like CDO^3 etc). He said that residual risk (that left after hedging with simpler products) should be measured and costed for. Bruno also mention the problems with assessing long term volatility where traders will try to set this input to what best suits their own P&L
• Leo Tilman said that risk management needs to be a decision-support discipline and not a policing function. He later suggested that risk managers should have to work as consultants for a while to understand that they get paid for serving the needs of the customer, not just stopping all activity/risks (in fairness to risk managers, I guess they might ask who is my customer? the trader? the CEO? the firm?).
• Dilip Madan added to the models debate by saying "what is not in the assumptions will not show up in the conclusions".
• Emanuel likes the old GS partner model for banking, and mentioned the example of Brazilian banks where banks/banking staff(?) did not enjoy limited liability. Dilip said he understood the advantage of this but no limited liability would stifle entrepreneurship.
• Leon Tatevossian said that post-crisis the relationship between risk managers and traders is better than before, and that there was also greater co-operation between empiricists and modelers. Leo add that risk managers and traders need to speak the same language and understand what each other means by "risk".
• Bruno said that models were much less of a problem than leverage.
• All seemed to agree that the tools were not invalidated by the crisis, but the framework in which they are used was the important thing.

 

 

 

Regulatory Dis-Harmony at Baruch College

Roberta Romano gave her presentation in the second session of the morning, putting forward her ideas that what was needed was greater regulatory dis-harmony rather than world-wide harmonisation. Fundamentally she argued that this diversity of approaches in different regulatory regimes would minimise the impact of regulatory error (since it would confine the error to less of the system) and it would provide a test bed for ideas so that it could be seen what regulations work and what do not.

Certainly there is some basis for this idea from others in the industry (see post on Pierre Guilleman concern's on the impact of Solvency II) and I first heard the idea of diversity in financial services put forward by Avinish Persaud at Riskminds a few years back (see post).

Roberta spent a good amount of the presentation putting forward how the process of putting this diverse regulation in place would work, with individual regimes applying to the Basel Committee putting forward why they wanted to deviate from Basel III and justify how such a desired deviation would not increase systemic risk. The Basel Committee would then have a short time frame for approval (say 3 months) and the burden of proof would be placed on the Committee to show that the deviation was a detrimental one. She also described how some of the home-host regulatory conflicts would be dealt with under her proposed process.

I thought that the overall aims of her proposal were sound (diversity leading to a more robust financial system) but the implementation process would be difficult to implement I would suggest and very open to regulatory arbitrage (both by banks and by countries seeking to boost their own economies). Roberta did touch on this, but my biggest criticism was that if one of the benefits was that for a while such a diverse system would demonstrate which regulations work and which do not, then logically everyone would eventually converge on the regulations that work, re-harmonising regulations and reducing diversity.This convergence would then introduce its own (potentially new?) risks and you would be back to where you started.

Panel Debate

A few points from the panel debate following the presentation:

• There was more criticism of how Basel regulations were gamed by the banks, particularly in relation to optimising Risk Weighted Assets
• One member of the panel pointed out that non-Basel US banks faired better in the crisis than those subject to Basel
• Rodgin Cohen suggested that RWA should receive more focus rather than the level of the capital charge (echoing the previous panel session).
• Rodgin was highly critical in the cutbacks in funding for regulators in the US
• Rodgin also said that London had its standing as the leading world financial centre due to the US Congress (refering to the Eurobond market and the Sarbanes-Oxley)
• Regulators should never forget that the "Law of Unintended Consequences" rules

 

 

Systemic Risk at Baruch College

Baruch College hosted the Capco-sponsored "Institute Paper Series in Applied Finace" on Thursday. I assume this is a further follow-up event to the one they did at NYU Poly last year (see some notes here). I have put some notes together below, my apologies in advance to the speakers for any innaccuracies or ommissions in putting my thoughts together:

Systemic Risk Presentation

First part of the day started with a presentation by Viral V. Acharya of Stern on systemic risk. I have always found systemic risk an interesting topic, given the puzzle of how do you dis-incentivise an organisation from increasing risks in the wider financial system when the organisation itself will not directly (or wholey) face the consequences of this "external" risk increase.

Viral started his presentation with some great jokey graphics, one of a the HQ of a bank going up in flames with fireman hosing the flames with banknotes not water. He mentioned the definition of systemic risk given by Daniel Tarullo, Governor of the Federal Reserve (I couldn't find the definition, but primer paper here). He asked how Lehman was allowed to fail when the likes of Fannie Mae, Freddie Mac, AIG, Merrills, CitiGroup, Morgan Stanley, Goldman Sachs, Washington Mutual and Wachovia were not and offered assistance in one way or another. He said there was not enough capital in the system to stop Lehmans failure but that he saw Lehmans as the catalyst for the recapitalisation of the American banking system, not the cause. He later implied that Europe had so far lacked such a catalyst for action in the European banking system.

Viral said that he wanted to put forward an ex-ante regulation that would force a bank to retain additional capital to account for the systemic risk it produced. He said that the banking system was obviously much safer than it had been a few years back, but suggested that whilst the system could now withstand say the failure of a large organisation such as Citigroup, in his opinion it would struggle to survive the failure of Citigroup and a Euro default happening at the same time. Viral said that the current Dodd-Frank regulation on systemic risk was not a healthy one in that if a large institution fails, banks of capitalisation of over $50B are jointly taxed to assist in the consequences of the failure. Viral viewed this as a big dis-incentive against a healthy bank (say a JPM) from stepping in to purchase the failing institution before the failure, as JPM would know that it would be taxed anyway on the bailout. 

In Viral's model, he defined a crisis as a 40% market correction, and assumed that non-equity liabilities repayed at face value in such a crisis. Given there is not much real data around for a 40% correction, he used data obtained from 2% correction events observed, then extrapolated from the 2% to the 40% level. He said that the question that needed to be asked was whether in such a crisis scenario that a bank like JPM would retain 8% capital. He emphasis that the level of capital chosen was somewhat arbitary but rather more importantly were the assumptions in the model of crisis, since the capital models used in regulation today are based on average losses not crisis-level losses. Using this and related models, Viral showed that the banks exhibiting the most systemic risk were Bank of America, JPM and the Citigroup (for more background and a complete list see Stern's V-Lab ).

Viral said the restructuring of Dexia (exposed heavily to peripheral sovereign debt) was the "Bear Stearns of Europe" (exposed heavily to peripheral MBS), but that is restructuring was not large enough to cause a more widespread re-capitalisation of the European banking system. Dexia was ranked as one of the safest banks in the Europe-wide stress tests of 2011, given that the Basel risk weightings did not apply any haircut to European sovereign debt. This was another critiscism that Viral levelled at Basel in that the risk weightings are static and do not reflect changes in market conditions.

Panel Debate

Viral then joined a panel debate on systemic risk chaird by Linda Allen of Baruch, joined by Jan Cave of FDIC, Sean Culbert of Capco, Gary Gluck of Credit Suisse and Craig Lewis of the SEC.I have tried to bring out some of the main themes/points of the discussions below:

- The Balance Between Risk to the System and Risk to the Economy

There was a lot of debate on the secondary effects of regulating systemic risk and increasing capital charges on banks, and its wider effect on the general economy. Craig put forward the argument that too high capital requirements would stifle lending and in turn stifle the wider economy (arguably the "bigger" systemic risk maybe?). He argued for a balance to be found and that the aim should not be to eliminate risk in the system completely. I guess Craig was taking the banker's view, but the rest of the panel seemed to agree that the point was a valid one.

- Basel III

All agreed that Basel III was an improvement but there was still much more to be done. Gary was critical of Basel III calculation remaining too static, but Jason described how Basel III had removed many debt-like assets from the capital calculation which was good however. Jason also described how Basel I had been a simple framework (and good for that) but was tinkered with with VAR encouraging assets to be moved to trading book to reduce capital charges. Basel II then introduced the Internal Model approach and over ten years capital requirements were continued to be lowered, with CDO's attracting a 56bp capital charge during this time down from 8%. Enforcement of Basel III on both liquidity risk and capital was considered as key for coming years.

- Liquidity Risk

There was general consensus that pre-2007 liquidity risk was not talked about enough and there were no standard ways of calculating its level. Jason said that pre-2007 the regulators had not modelled what happens when the counterparties start running. Gary said that he questioned whether some of the current calibrations of liquidity risk were correct.

- Volcker

Sean raised the point that Volcker was likely to impact market-makers and hence impact liquidity (see earlier post on this).

- Rehypothecation

Sean also mentioned that Rehypothecation of Assets has not been debated enough and had only received scant attention in Dodd-Frank (maybe see recent article on Thomson-Reuters on MF Global)

- Europe (and more Basel)

General consensus that Basel III capital requirements will constrain GDP growth in Europe. Viral seemed to have the strongest views here, saying the Europe needed a bank recapitalisation program just as the US had gone through, and that such a program would be a big boost to economic confidence. Viral remains deeply sceptical on the success of Basel III - for example all of the 2007 failures were supposedly from well capitalised insitutions under Basel I and II. Viral says that the problem is not the level of capital (8% or 12% etc) but the method of modelling the shock. A good point from Gary I thought was his premise that politics in relation to sovereign debt was playing its part in undermining the calculations and approach of Basel III.

- Too Big to Fail?

One audience question was "is too big to fail simply too big?" and should the largest organisations be broken up into more manageable parts. Viral answered that he was not in favour of a size constraint and cited that some large institutions, notably JPM, Rabobank and HSBC had been relatively robust successful during the recent crisis. He did however qualify this response by saying that he was in favour of a size constraint if the large size reached was due to implicit banking guarantees from the government, and that he would like failing large banks to be broken up into smaller pieces.

 

PRMIA - Operational Risk, Big Data and Human Behaviour

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

Definition of Operational Risk

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

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

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

Beyond Box Ticking for Operational Risk

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

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

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

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

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

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

Duplication of Effort

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

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

Audience Questions:

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

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

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

Summary

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

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

 

 

 

 

 

 

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

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

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

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

 

The financial crisis and Andrew Lo's reading list

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

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

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

Pandit on Comparing Apples and Risk

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

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

PRMIA - From Risk Measurement to Risk Management by Samuel Won

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Introductory discussion

Andrew Delaney, Amir Halton (Oracle)

Drivers of the data management problem – regulation and performance.

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

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

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

Impact of regulations on Enterprise Data Management requirements

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

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

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

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

· Reporting – needs to be reliable and meaningful

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

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

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

Technology challenges of building an enterprise management infrastructure

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

Coverage and reporting were mentioned as the biggest challenges.

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

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

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

· Consistency of data including transaction data.

· Data coverage.

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

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

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

Better business agility through data-driven analytics

Stuart Grant, Sybase

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

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

Data collection, normalisation and orchestration for risk management

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

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

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

Integrating data for high compute enterprise analytics

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

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

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

Internal model approval, risk management and regulatory compliance

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

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

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

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

 

 

 

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

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

No worries! 

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

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

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

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

 

PRMIA on Data and Analytics

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

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

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

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

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

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

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

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

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

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

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

Removing the punchbowl at NYU-Poly

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

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

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

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

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

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

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

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

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

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

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

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

Taleb and Model Fragility - NYU-Poly

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

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

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

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

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

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

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

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

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

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

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

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

IKEA and Market Risk Management – Choice is a worrying thing!

Risk management and data control remain at the top of the agenda at many financial institutions. Many have said that the recent crisis highlighted the need for more consistent, transparent, high quality data management, which I totally agree with (but working for Xenomorph, I would I guess!). Although the crisis started in 2007, it would seem that many organizations still do not have the data management infrastructure in place to achieve better risk management.

I moved apartment last week and had to face the terrifying prospect of visiting IKEA to buy some new furniture. On walking through the endless corridors of furniture ideas I wondered whether the people at major financial institutions feel as I did: I knew I needed two wardrobes, I knew the dimensions of the rooms, I knew how many drawers I wanted. Then I got to the wardrobes showroom, sat in front of the “Create your own wardrobe” IKEA software and the nightmare started. How many solutions are there to solve your problems? And how many solutions, once you get to know of their existence, make you aware of a problem you didn’t know you had? That’s how I spent 2 days at IKEA choosing my furniture and still I wonder whether in the end I got the right solution for my needs.

Coming back to risk management, I imagine the same dilemma may be faced by financial institutions looking to implement a data management solution. How many software providers are out there? What data model do they use? Are they flexible enough to satisfy evolving requirements? How can we achieve an integrated data management approach? Will they support all kind of asset classes, even the most complex? 

In these times of new regulations where time goes fast and budget is tight, selection processes have become more scrupulous. 

As often happens in life, when we need a plumber for example, or a new dentist, we look for positive recommendations, people willing to endorse the efficiency and reliability of the service. So, with this in mind, please take a look at the case study we put together with Rabobank International, who have been using our TimeScape analytics and data management system at their risk department since 2002 for consolidated data management. More client stories are also available on our website here: www.xenomorph.com/casestudies. 

I hope that many of you will benefit from reading the case study and for any questions (on IKEA wardrobes too!), please get in touch...

 

More formal management of instrument valuation needed

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

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

2010 Risk in Review NY

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

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

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

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

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

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

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

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

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

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

Of his time, he said he spent:

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

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

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

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

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

 

Dodd Frank Regulation - being seen to be doing something?

I went along to a Six Telekurs event "Securities Valuations: Is the Price Right?" last week - good event with some interesting speakers, most notably Paul Atkins of Patomak Partners to talk about the Dodd-Frank Wall Street Reform and Consumer Protection Act 2010. Paul is based out of Washington and was not very complimentary about what has been going on.

He started by saying that the Act was very large in size, with over 2319 pages (compared to SarbOx with only 60) and given this size he suggested that you could guess how many in Congress had actually read it. Background to the Act were:

• "Political Tailwinds" such as:
  • New Democrat Government with tenuous majority
  • Ambitious legislative plans
  • Bleak economic back-drop
• An angry populace:
  • TARP bailouts/Wall St bonuses
  • Recession and high unemployment
  • Perception that Govt. contributed to crisis
• Aggressive case for new regulation based on:
  • Lack of confidence in current systems and regulation
  • "Too big to fail" demonstrating that regulators lack the toolsets necessary to deal with such events
  • High leverage across the financial system and the economy
  • Poor risk management by existing participants
  • Opaque shadow banking system and opaque derivatives markets

He summarised that Housing and the Credit Rating Agencies were the key fundamentals behind the financial crisis.

Paul said that with the new regulation had the following features:

• The Act is a sweeping revision of financial regulation in the US
  • few dodged the regulatory changes (notably insurance managed to do this)
• The Federal Reserve has emerged pre-eminent amongst all regulatory bodies in the US.
• Significant discretion has been yielded to regulators to work out specifics
• Sheer size and ambiguous wording of the Act exacerbates the uncertainty in the market and economy and will require further fixes over coming years
• The Act does not reform Government Sponsored Enterprises (Fannie Mae, Freddie Mac)
• Far from reducing/simplifying the number of agencies involved in regulation the Act eliminated 1 agency and created 13 more
• Paul asked the question whether spreads and volatility will rise in the market due to new regulation (such as the Volcker rule) and whether ultimately this will trickle down to hinder or benefit SMEs.
• The Act will likely result in regulatory arbitrage opportunities and Paul said this was not a good thing for the United States

Paul said that in his view Congress learned the wrong lessons from the crisis:

• No reform of Fannie Mae and Freddie Mac
• Government Housing Policy left unaddressed
• Transparency still lacking despite efforts from FASB on fair value
• International Policy Co-ordination is still an open question as to its extent
• No reform of existing regulator structures
• The crisis has resulted in payoffs to favoured groups (Unions, Trial Lawyers etc)

Paul talked about how hedge funds and private equity funds were going to experienced increased regulation with them having to register if they have over $100M assets under management and future implications for systemic risk provisions. He mentioned that Venture Capital investments had escaped being required to register if the lock-up period was over 2 years.

He briefly discussed the coming changes in OTC derivatives on centralised clearing, post trade reporting and new liability provisions. Paul was also concerned about certain SEC related issues such as "Whistleblower" provisions which contain a bounty programme of about 10-30% of any fine subsequently awarded against a financial institution. He re-iterated that it was not yet clear what all of the bodies involved in regulation would be doing, and at the same time as this was the case the very same bodies were also being given very strong powers such as that of legal subpoena.

Paul was a very knowledgeable speaker and had some good points to make. Listening to him speak it would seem from my perspective that the Act is a prime example of "being seen to be doing something" to address the crisis rather than something better structured, with all of "law of unintended consequencies" risks that such an initiative entails.

 

 

 

Transparency Regulation is not Transparent.

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

The Humans Between Risk and Data

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