Help us, Nate Silver!
Statistics cannot be any smarter than the people who use them. And in some cases, they can make smart people do dumb things. One of the most irresponsible uses of statistics in recent memory involved the mechanism for gauging risk on Wall Street prior to the 2008 financial crisis. At that time, firms throughout the financial industry used a common barometer of risk, the Value at Risk model, or VaR. In theory, VaR combined the elegance of an indicator (collapsing lots of information into a single number) with the power of probability (attaching an expected gain or loss to each of the firm’s assets or trading positions). The model assumed that there is a range of possible outcomes for every one of the firm’s investments. For example, if the firm owns General Electric stock, the value of those shares can go up or down. When the VaR is being calculated for some short period of time, say, one week, the most likely outcome is that the shares will have roughly the same value at the end of that stretch as they had at the beginning. There is a smaller chance that the shares may rise or fall by 10 percent. And an even smaller chance that they may rise or fall 25 percent, and so on.
On the basis of past data for market movements, the firm’s quantitative experts (often called “quants” in the industry and “rich nerds” everywhere else) could assign a dollar figure, say $13 million, that represented the maximum that the firm could lose on that position over the time period being examined, with 99 percent probability. In other words, 99 times out of 100 the firm would not lose more than $13 million on a particular trading position; 1 time out of 100, it would.
Remember that last part, because it will soon become important.
Prior to the financial crisis of 2008, firms trusted the VaR model to quantify their overall risk. If a single trader had 923 different open positions (investments that could move up or down in value), each of those investments could be evaluated as described above for the General Electric stock; from there, the trader’s total portfolio risk could be calculated. The formula even took into account the correlations among different positions. For example, if two investments had expected returns that were negatively correlated, a loss in one would likely have been offset by a gain in the other, making the two investments together less risky than either one separately. Overall, the head of the trading desk would know that bond trader Bob Smith has a 24-hour VaR (the value at risk over the next 24 hours) of $19 million, again with 99 percent probability. The most that Bob Smith could lose over the next 24 hours would be $19 million, 99 times out of 100.
http://www.salon.com/2013/01/06/help_us_nate_silver/
= new reply since forum marked as read
