Simon Johnson and James Kwak take a look at how VaR got to be so popular in the first place. They make the insightful observation that a bad (or at least an incomplete) model can gain acceptance not only because of its simplicity but, oddly, because of its output as well.
Indeed, VaR succeeded not just because it seemed to capture risk accurately ("losses exceeded only 5% of the time" and so on), but because it provided the answer that financial agents were looking for. Most cynically, its greatest disadvantage - failing to look at what actually happens in crisis times, rather than just defining the crisis itself - turned into its biggest sell point when it came to market adoption. In a bizarre twist, the model was chosen because it gave the right answer; not because it answered the right question.
It reminds me of a passage from the ever-insightful Hitchhiker's Guide to the Galaxy:
"I checked it very thoroughly," said the computer, "and that quite definitely is the answer. I think the problem, to be quite honest with you, is that you've never actually known what the question is."
"But it was the Great Question! The Ultimate Question of Life, the Universe and Everything!" howled Loonquawl.
"Yes," said Deep Thought with the air of one who suffers fools gladly, "but what actually is it?"
But you don't come here for HHG2G quotes (or do you?). Here's the key excerpt from Johnson and Kwak's analysis:
David Colander made this point about economic models: The sociology of the economics profession gave preference to elegant mathematical models that could describe the world using the smallest number of parameters. “Common sense does not advance one very far within the economics profession,” he says.
A similar point can be made about VAR models. Sure, maybe all the financial professionals who design and work with VAR know about its shortcomings, both mathematical and practical. But nevertheless, using VAR brought concrete benefits to specific actors in the banking world by helping them rationalize bad bets. If common sense would lead a risk manager to crack down on a trader taking large, risky bets, then the trader is better off if the risk manager uses VAR instead.
Not only that, but imagine the situation of the chief risk manager of a bank in, say, 2004. As Andrew Lo has argued, if he tried to reduce his bank’s exposure to structured securities such as collateralized debt obligations, he would be out of a job; VAR gave him a handy tool to rationalize a situation that defied common sense but that made his bosses only too happy. And at the top levels, chief executives and directors who probably did not understand the shortcomings of VAR were biased in its favor because it told them a story they wanted to hear.