For a complete list of Beginners articles, see the Financial Crisis for Beginners page.
Joe Nocera has an article in today’s New York Times Magazine about Value at Risk (VaR), a risk management technique used by financial institutions to measure the risk of individual trading desks or aggregate portfolios. Like many Magazine articles, it is long on personalities (in this case Nassim Nicholas Taleb, one of the foremost critics of VaR) and history, and somewhat light on substance, so I thought it would be worth a lay explanation in my hopefully by-now-familiar Beginners style.
VaR is a way of measuring the likelihood that a portfolio will suffer a large loss in some period of time, or the maximum amount that you are likely to lose with some probability (say, 99%). It does this by: (1) looking at historical data about asset price changes and correlations; (2) using that data to estimate the probability distributions of those asset prices and correlations; and (3) using those estimated distributions to calculate the maximum amount you will lose 99% of the time. At a high level, Nocera’s conclusion is that VaR is a useful tool even though it doesn’t tell you what happens the other 1% of the time.
naked capitalism already has one withering critique of the article out. There, Yves Smith focuses on the assumption, mentioned but not explored by Nocera, that the events in question (changes in asset prices) are normally distributed. To summarize, for decades people have known that financial events are not normally distributed – they are characterized by both skew and kurtosis (see her post for charts). Kurtosis, or “fat tails,” means that extreme events are more likely than would be predicted by a normal distribution. Yet, Smith continues, VaR modelers continue to assume normal distributions (presumably because they have certain mathematical properties that make them easier to work with), which leads to results that are simply incorrect. It’s a good article, and you’ll probably learn something.
While Smith focuses on the problem of using the wrong mathematical tools, and Nocera mentions the problem of not using enough historical data – “All the triple-A-rated mortgage-backed securities churned out by Wall Street firms and that turned out to be little more than junk? VaR didn’t see the risk because it generally relied on a two-year data history” – I want to focus on another weakness of VaR: the fact that the real world changes.