Iksil, big bets, VaR and JPM
As we suggested back when the JPM whale trade was announced, the trade(s) was supervised by hopefully more than one risk manager, with the trades being that large. Bloomberg reports that Mr Iksil traded big size before, and was used to carry more VaR solo, than the bank in total. VaR or not, the question is most probably as, often in finance, “Did the bosses understand what kind of trades Iksil was putting on,( and what the closing cost would be)”? From Bloomberg.
Iksil’s value-at-risk, a measure of how much a trader might lose in one day, was typically $30 million to $40 million even before this year’s buildup, said the person, who wasn’t authorized to discuss the trades. Sometimes the figure, known as VaR, could surpass $60 million, the person said. That’s about as high as the level for the firm’s entire investment bank, which employs 26,000 people.
Investigators are examining how long senior executives knew about Iksil’s swelling bets at the chief investment office before losses approached $2 billion. One focal point is why the formula used to calculate Iksil’s VaR was altered early this year, cutting the reported risk by half. The change followed an internal analysis in late 2011 and was approved by top risk executives, said a person close to the bank. About the same time, half a dozen managers typically involved in such decisions moved to new jobs.
“If it was something that had that large an impact, it would have to be agreed to at the very-most-senior level within risk management,” probably including the bank’s chief risk officer, said Steve Allen, a former head of risk methodology for JPMorgan who retired in 2004. “You’re not going to make a change of that magnitude on the basis of one risk manager.”
The Flaws of Finance
With the ever increasing number of black swans hitting the world, there is a rapid increase of critique with regards to different models the financial industry uses in determining risk and how to hedge IT. Must read by James Montier on the subject.
The National Rifle Association is well-known for its slogan “Guns don’t kill people; people kill people.” This sentimenthas a long history and echoes the words of Seneca the Younger that “A sword never kills anybody; it is a tool in the killer’s hand.”
I have often heard fans of finanancial modelling use a similar line of defence. However, one of my favourite comedians, Eddie Izzard, has a rebuttal that I find most compelling. He points out that “Guns don’t kill people; people kill people, but so do monkeys if you give them guns.” This is akin to my view of financial models. Give a monkey a value at risk (VaR) model or the capital asset pricing model (CAPM) and you’ve got a potential financial disaster on your hands.
I would take grey hair over algorithms any day
Guest post by Steen Jakobsen.
The farther backwards you can look, the farther forward you are likely to see – Winston Churchill
During the ERM crisis in 1992 I was a still a relatively young trader and had the good fortune to witness some of the best risk takers in the world – the Susquehanna group – who had a joint venture with my employer, the Chase Manhattan Bank. I learned more during that ERM crisis in 1992 than I have in the rest of career.
The main lesson I learned is that being short gamma can wipe you out in a hurry. During the ERM crisis, Swedish interest rates touched 500% USD-Deutschmark was moving 10-15 figures in a matter of hours. Everyone was trying to make sense as the uncertainty saw market volatility spinning out of control.
Why is this relevant? Well partly because, despite the 2008 crash, few traders of today really understand risk and risk management. They persist in believing that “Value at risk” – or VaR – offers a true picture of the amount of risk exposure in the market – manage your VaR and you are okay. But times like 1992, the 2000, 2008 – and maybe even 2012 – show us that why VaR doesn’t work. That’s because VaR doesn’t properly quantify or describe the “tail risk”, that small percentage of the time when markets doe what the statistical models don’t account for: go crazy.
In crude terms, VaR explains to you the risk of the volatility on any given day with 95 per cent certainty. But what about the remaining 5 per cent? That’s where the VaR model quickly breaks down, because at times when markets run into a truly volatile patch, pricing becomes downright discontinuous as liquidity dries up – the market moves in astound gaps rather than in the normal step-wise fashion of more normal times.
As the prices jump around, the correlations upon which 90 per cent of VaR is built simply collapse. And if you are caught the wrong way – there is no market and there is no hedge as everything moves quickly and in the same direction.
JPM-Hit by the limits of statistics?
We are amazed by the many articles discussing (and giving advice) the JPM “perfectly hedged” trading CIO book, by mainly journalists that don’t understand risk, trading nor (the) greeks. JPM, claiming to have one of the world’s most sophisticated risk management, has apparently lost at least 2 billion USD. What actually happened will probably never hit the media. While journalists debate whether or not to drop VaR as a risk measure, we think it is appropriate to review an old article from Nassim Taleb, one of few clever minds when it comes to understanding “real” risks. From Taleb’s Fourth Quadrant, via Edge.
Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).
The current subprime crisis has been doing wonders for the reception of any ideas about probability-driven claims in science, particularly in social science, economics, and “econometrics” (quantitative economics). Clearly, with current International Monetary Fund estimates of the costs of the 2007-2008 subprime crisis, the banking system seems to have lost more on risk taking (from the failures of quantitative risk management) than every penny banks ever earned taking risks. But it was easy to see from the past that the pilot did not have the qualifications to fly the plane and was using the wrong navigation tools: The same happened in 1983 with money center banks losing cumulatively every penny ever made, and in 1991-1992 when the Savings and Loans industry became history.
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