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The Formula That Killed Wall Street
We recently discussed the perspective that the harrowing of Wall Street was caused by over-reliance on computer models that produced a single number to characterize risk. Wired has a piece profiling David X. Li, the quant behind the formula that enabled the creation of such simple risk models. "For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels. His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. ... [T]he real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust."
In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.
Citation? Booms and busts are caused by, respectively, expansion and contraction of the money supply (usually in the form of bank credit), often accompanied by manipulated interest rates. The formulas used by lots of investing firms could cause clusters of errors, but the extent of types of companies (and governments) affected points to a more Austrian-style, systemic boom/bust rather than a single-(important-)sector miscalculation.
That Gaussian curves are a poor model for unlikely events has been known for quite some time. This is best explained by Nassim Taleb in the following books:
His main thesis is that the markets are essentially random and are basically impossible to predict in any meaningful way. Further there are unlikely unknown unknowns can cannot be predicted until the they occur, usually with disastrous consequences.
---- It won't be as bad as you fear or as good as you hope, but it will take twice as long as you plan.
Is global warming the new replacement for Godwin's Law?
Do you even lift?
These aren't the 'roids you're looking for.
>>There is nothing wrong with using a model. Models are good.
Not in economics, they're not. The book Black Swan, which should be read by anyone interested in this topic, says that the hideous lie is that people claim that "they're better than nothing", when, in fact, they're worse than not having any model at all.
The LTC crash was caused by the founders (Nobel Laureates in Economics) having a model to quantify risk. IIRC, they used some sort of guassian model, taking the standard deviation of price movement as "risk". (http://en.wikipedia.org/wiki/Black-Scholes#Black.E2.80.93Scholes_model) This of course looked good until, quite suddenly, it wasn't and there was an event that their model predicted shouldn't have happened within the lifetime of the universe (that's the problem with using gaussians instead of cauchy curves or other fat-tailed distributions) and the company crashed and burned, and did a lot of collateral damage as well.
From the wikipedia article on LTC (http://en.wikipedia.org/wiki/Long-Term_Capital_Management): Merrill Lynch observed in its annual reports that mathematical risk models, "may provide a greater sense of security than warranted; therefore, reliance on these models should be limited."
Exactly. EVERY model that only sees rising house prices during it's data collection phase WILL assume that house prices will keep rising, and therefore tell bankers that dodgy mortgages are ok.
After all, as long as house prices keep rising, there is NO risk whatsoever in dodgy mortgages. Either you get the stated intrest (buyer pays mortgage) or you get the price rise of the house since the buyer bought it with your money (in the case of default) ... the risk of losing money in the deal is EXACTLY the chance that house prices drop. And house prices never dropped (significantly) in over 50 years ... obviously any statistical algorithm would have told you the risk was minimal.