NYTimes Article on Risk Management

Via Schneier, I came upon this New York Times article, which talks about the use and abuse of everybody’s favorite quant tool: Value at Risk (VaR). One particular section caught my eye:

…the big problem was that it turned out that VaR could be gamed. That is what happened when banks began reporting their VaRs. To motivate managers, the banks began to compensate them not just for making big profits but also for making profits with low risks. That sounds good in principle, but managers began to manipulate the VaR by loading up on what Guldimann calls “asymmetric risk positions.” These are products or contracts that, in general, generate small gains and very rarely have losses. But when they do have losses, they are huge.

I find this interesting: reporting and acting on VaRs is not different from reporting and acting on the results of any other probabilistic model. Engineers and operations researchers do it all the time when modeling the failure rates of process units and finished products or services. The same applies to variations in process inputs (crude oil composition for a refinery, particle size distribution for a powdered pharmaceutical drug and so on). Even something as mundane as the residence time distribution in a reactor is a probabilistic model that important decisions are based on. Yet the failure modes are usually not catastrophic system meltdown. Why?

There are two questions I cannot fully answer yet: It is relatively easy to cross-check an engineering model with theory using back-of-the-envelope estimates from first principles. Is the same possible for financial models? It is also relatively easy to cross-check a model with data from controlled experiments, not just observations seen in the wild with confounding factors. Are controlled experiments feasible and practical for financial systems?

Of course, no model would be complete without a car analogy:

David Einhorn, who founded Greenlight Capital, a prominent hedge fund, wrote not long ago that VaR was “like an air bag that works all the time, except when you have a car accident.”


One thought on “NYTimes Article on Risk Management

  1. For q1 (verifying models using theory): The world of finance does not have the kind of rules that the physical world enjoys. So I don’t think it is possible to do much in terms of model validation based on “first principles”. It is possible to do monte carlo simulation and get some ideas on what will happen to the model under different circumstances.

    For q2 (verifying models against data): In the physical world, if a small object exerts some force then an object twice the size will most certainly exert a force proportional to the increased size. But the funny thing about humans or the world of finance is that what might work at the level of a village might not work on a nationwide scale (issues of trust, service time etc.). On the other hand, things that don’t work at a small scale might very well work at a bigger scale due to economies of scale. So evaluating models using data is an even bigger crapshoot than learning something about the model by simulation.

    I have to say, regardless of the proofs from theory or data, the mode of failure for almost all systems is human (mis)judgment. People will do what they have always done: ignore things if they are convinced that there is a small chance that they could benefit by ignoring the model and doing something else.

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