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њИМИ ММ< .ММааММММ r ММ0рР xјОааЖjRP 1.5 copy: opinion. Risk Professional, July/August, 1999, p. 14.
Headline: Its time we buried Value-at-Risk
Length: 1,560 words
Pics: Photo of Richard Hoppe, perhaps a burying VaR illustration?
Intro:
The powerful industry consensus behind VaR cannot hide the fact that the measure rests on statistical assumptions that do not correspond to the real world, says Richard Hoppe. The results of VaR calculations are thus literally nonsensical but he suspects it will probably take another market crisis before the financial services industry comes to its senses.
Body:
Lots of people have a vested interest in VaR. Regulators set standards for it, vendors sell software for it, and managers believe its estimates and even demand them. VaR has become an industry, and to judge from some of the reactions to a critical article I published last year, something of a cult. Im not alone in that assessment. One correspondent, a risk manager, told me of having evoked what he called VaR hysteria from some VaR proponents when he ventured to question its applicability to his risk management problem.
This consensus obscures the fact VaR is grounded firmly in orthodox variance-based linear statistics and probability theory, whose application to market risk estimation is founded on the assumption that markets are Gaussian random walks. But the assumption that markets behave in this fashion is highly questionable, which suggests that a risk measure based on such assumptions is of little or no value.
Questionable assumptions
The industry standard use of VaR was outlined in these pages earlier this year by Andrew Fishman in his tutorial article A Question of Confidence (Risk Professional, April 1999, page 43). He described how to scale a VaR value calculated for one confidence interval and time horizon to produce VaR estimates for different confidence intervals and time horizons. He mentioned that the scaling techniques depend on the assumption of normality of distributions of market returns and noted that the normal distribution is a pragmatic approximation.
It is not my intention here to specifically criticise Fishman he is presenting the standard approach to VaR. Rather, because his paper contains particularly clear language concerning the assumptions and methods underlying VaR estimation, it provides an ideal vehicle for a critical look at VaR estimation.
Fishman noted that practitioners should be aware that the statistical assumptions used in VaR calculations are sometimes criticised. This point deserves clarification: what critics argue is that the actual behaviour of financial markets does not correspond to the assumptions underlying the mathematical theory. Therefore the numbers one calculates from market data, using linear variance-based statistics and a normal probability density function, do not mean what most practitioners believe they mean. The statistical operations are not veridical maps of the real world they are alleged to model.
Risk managers all too often underestimate the problems associated with extending VaR statistics calculated for one time horizon and confidence interval to other horizons and intervals. In fact, VaR estimates in general (and, a fortiori, multiplicative scalings of VaR estimates) depend on much more than just the normality assumption. In addition to normality, they make three questionable assumptions: that markets exhibit stationarity; that market prices are serially independent; and that only linear relationships between markets are relevant.
Stationarity and stability
In the statistical sense (ie probabilistic thermodynamic models, such as those of Brownian motion in tea kettles), the stationarity assumption means that the mean, variance, skew and kurtosis of the underlying population distribution are taken to be stable through time. In the dynamical sense (ie differential calculus models, such as those of planetary orbits and rocket trajectories), it means that the forms of the equations that describe a systems dynamics are assumed to be constant through time. F = ma yesterday, today, tomorrow and forever, regardless of the values of m and a at the moment.
Now consider what the stationarity assumption means in the real world that VaR models purport to represent. Translating the assumption into the real world, one must assume that market participants do not learn from experience, do not alter their behaviour in the light of events and are mindless hard-wired automata with infinitely long time horizons. (Probability is defined as frequency in the long run, but some of the runs can be very long ask any trader.) Further, one must accept the preposterous notion that the economic, political, and regulatory environments in which markets are embedded are constant, or at least predictable. (Which sovereign state defaulted-by-restructuring late last year?)
Assuming serial independence means that one believes that what happened yesterday or the day before has no implications for what will happen today or tomorrow. Once again, in the real world one must believe that market participants are incapable of learning from experience and have no expectations about tomorrow to change in the light of yesterdays events. (Is anyone going to casually buy that states debt this year?)
The assumption of linearity of relationships means that only linear correlations between markets are believed to be operative and that potential non-linear relationships (including non-differentiable and discontinuous functions) are not relevant. One must assume that real market participants are linear information processors that do not abruptly change their minds, or shift their attention from this variable to that, or suddenly alter their interpretation of the relationship between, say, bonds and equities, or unemployment and inflation.
If real-world markets do not meet the assumptions of the mathematical theory that is employed to model them, then the outcomes of calculations cannot be interpreted to say anything meaningful about market phenomena the results are literally nonsense. It is exactly as though one had created sentences by manipulating Urdu words according to the rules of English syntax. The results would be gibberish to both an English speaker and an Urdu speaker.
How bad is the gibberish?
So, there are good grounds to seriously question the validity of all four mathematical assumptions with respect to markets. The assumptions force a theory of the behaviour of market participants that seems to me a cognitive psychologist -- to be implausible and invalid on the face of it. Moreover and more serious for practitioners and managers in general we do not know the consequences of violating the assumptions, either precisely or even approximately. We do not know how bad the gibberish really is.
When errors due to violations of the assumptions are amplified by techniques like multiplying a one-day estimate by the square root of time to generate a 10-day estimate, the error of the new estimate is anyones guess. Anyone with a deep enough database of market returns can easily test the accuracy and reliability (ie consistency through time) of the scaling of the one-day standard deviation of returns to longer intervals. Doing so will considerably reduce ones confidence in the numbers that the rescaling produces. Multiplying fuzz by the square root of time merely magnifies fuzz.
Because we have good grounds to believe that the mathematical assumptions do not hold for markets, and because we do not know the consequences for VaR estimates of violating the assumptions, meaningful interpretation of a VaR estimate is impossible, especially interpretation of a rescaled-by-multiplication estimate. If one does not know how erroneous ones estimate is, if one cannot say how inaccurate or unreliable it is, then what interpretation except I dont know can be honestly defended?
The pseudo-pragmatism of assuming normality derives from the fact that unless one assumes that market returns are stationary Gaussian random walks and are devoid of any sort of serial dependence and are characterised solely by linear correlations among markets, one cannot use any of the apparatus of variance-based normal-distribution statistics and probability theory.
One might have to use (horrors!) weaker non-parametric techniques, or even (perish the thought!) openly admit that the problem as stated cannot be adequately addressed with existing techniques instead of burying that knowledge in a blizzard of technical obfuscation. The hardest thing for me to learn in 20 years as a professor was to say I dont know when I didnt know. But that turns out to be the most truthful thing one can say in many situations. I believe this is one such situation.
Will reason prevail?
Sooner or later, one may hope, reason will prevail. Or will it? Maybe that assumption about people not learning from experience isnt wrong after all. Does anyone remember portfolio insurance? It turned out that real market behaviour did not correspond to the theoretical assumptions underlying portfolio insurance. For one thing, market prices do not change continuously. But that wasnt the reason portfolio insurance was abandoned: it took a market crash to demonstrate its deficiencies.
Or, for that matter, does anyone remember Long-Term Capital Management? According to recent press reports, LTCMs VaR model estimated the loss through August 1998 to be a 14 sigma event. Probabilistically, a 14 sigma event shouldnt occur once in the lifetime of this universe. Seeing a 14 sigma event is something like seeing all of the molecules in a pitcher of water spontaneously segregate themselves into fast-moving and slow-moving columns, creating a pitcher that is boiling hot on the left side and freezing cold on the right.
Not incidentally, the LTCM VAR case was not the first cosmically unlikely event in the markets, nor even the most improbable. Calculated against the 250-day standard deviation of daily log returns, the October 19, 1987, U.S. stock market crash created a one-day 28 sigma close-to-close decline in near-month S&P futures prices. The occurrence of two unimaginably improbable events within 11 years of each other is compelling evidence that something is rotten in the foundations of the statistical edifice that produced the probability estimates.
Once again, for LTCM reason didnt prevail; it took a catastrophe to drive the lesson home, if indeed it has been learned. Unfortunately, there is powerful momentum for the use and extension of VaR and VaR-based estimation technologies. I fear it is too late to bury VaR; all one can do is wait it out, meanwhile watching very carefully where one puts ones money.
Richard Hoppe spent the 1960s in the aerospace and defence industry, before becoming Professor of Psychology at Kenyon College in Ohio in the 1970s and 1980s. He has been associated with the trading industry since 1990 as a consultant in artificial intelligence and as a trader. Richard is currently a principal of IntelliTrade, a market risk decision support firm. He trades derivatives privately and doesnt use VaR to manage his trading risks.
ends
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