Is all quantitative financial risk management bunk?
The comments to my post here last week on Benoit Mandelbrot were for the most part significantly more sophisticated than the post itself. So, since my days at Chez Felix are numbered, I thought I should avail myself of the brilliance of his commenters while I still can to ask a very basic question: Is the practice of quantitative financial risk management one big con job?
That’s one of the key arguments in Amar Bhidé’s new book A Call for Judgment: Sensible Finance for a Dynamic Economy. He says in the book that the approach to risk management that grew out of Harry Markowitz’s portfolio theory, Bill Sharpe’s Capital Asset Pricing Model (yes, I know it wasn’t just his, but he was the first to publish) and Fischer Black, Myron Scholes, and Robert Merton’s option-pricing model—all of which netted Nobels for their (still-living) creators—is fatally flawed because it depends on predictions about future volatility, and no one knows how to predict future volatility.
The funny thing is that the Markowitz/Sharpe/Black/Scholes/Merton approach arose in part out of the realization that it’s really hard to predict the trajectory of an individual stock—and that even if you did figure it out, others would start to imitate you, eventually affecting the the trajectory of the stock and rendering your predictions invalid. A stock’s future volatility might not be easy to predict, they reasoned, but it was much easier to predict than the stock’s future return. Bhidé turns that argument on its head:
Forming reasonable, subjective estimates of a stock’s return can be a challenging exercise. Predicting whether and by how much IBM’s price will appreciate requires researching and thinking about several factors such as its project plans, relationships with customers, existing and potential competitors, exchange rates, and the strength of the economy. With volatility, because there is no sensible way to think about what it should be, there is almost no choice but to take the “easy way out”: Calculate historical volatility. Shade to taste.
Now the objection one often hears from those in the financial world is that practitioners have moved on from the simple volatility models of the 1960s and 1970s. And that they have. But the models they use still assume that they know how to predict volatility, right? Is there evidence that anybody actually knows how to do that—not just short-term but through an entire market cycle? Or is everybody just shading to taste?
Another issue with quantitative risk models that even many of their creators acknowledge is that when a particular method of modeling risk becomes popular enough, it begins to affect market behavior and creates new risks that the model cannot see. I think this may have been a major contributing factor to every financial panic of the past 25 years, starting with the 1987 crash. (Well, except maybe the dot-com bubble and crash. I can’t really blame that on risk models. Although it is perhaps telling that the dot-com collapse wasn’t really a panic.)
You can’t prove the cause-and-effect, but it is clear that financial risk models have repeatedly broken down after years of seeming success. Which shouldn’t be all that surprising: It is in the nature of financial markets that every good (that is, money-making) idea eventually becomes a bad one. The difference between a momentum-investing formula and a risk-management model is that a risk-management model is supposed to, um, manage risk. And so I guess the question remains: Are all financial risk-management models ultimately a joke?
I really don’t know the answer. That’s why I’m asking you folks.