The formula that killed Wall Street
This would be why investment firms thought they could safety create these vast assemblages of mortgage debt that have gone so awry. Sometimes people really can be too clever for their own good.
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. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.
http://www.wired.com/techbiz/it/magazine/17-03/wp_quant?currentPage=all
Excellent article. Should be required reading for anyone working with financial models of any sort.
Even at a simpler level it's important to understand that the world is not normally distributed [in other words "we're skewed"], the tail is fatter than you think, VAR doesn't work very well, and black swans do exist (I highly recommend Taleb's book of that title).
We can't all be so-called financial experts. Therefore a good rule of thumb for investing that has stood me well over time is:
If it's too fucking complicated to understand, and doesn't make common sense, don't go there.
It's that kind of thinking that got Galileo and Darwin excommunicated.
The formula (and the math behind it) is not simple. But it produces a simple answer - a single number. You don't need much of a technical background to understand what default correlation is. You do need a significant amount to understand the limitations in the formula as well as the implications of using CDS pricing as a proxy for default probabilities (common sense would say the market can't get it right all the time and, perhaps just as importantly, even if the market is right CDS pricing is just an estimator of the "true" default probabilities - that estimator will have it's own distribution with its own mean and variance).
At the risk of digressing, this is a problem with most of the financial models I've had to work with over the years. Virtually all of them look at the mean and variance of some sort of sample and plug those into the model as being representative of the true mean and variance of the population. Not true (if the actual distribution is skewed, the sample mean is generally less than the true mean - that observation alone has significant implications in many areas).
The Financial Crisis and the Systemic Failure of Academic Economics*
http://www.debtdeflation.com/blogs/wp-content/uploads/papers/Dahlem_Report_EconCrisis021809.pdf
6. Conclusions
The current crisis might be characterized as an example of the final stage of a well-known boom-and-bust pattern that has been repeated so many times in the course of economic history. There are, nevertheless, some aspects that make this crisis different from its predecessors: First, the preceding boom had its origin – at least to a large part – in the development of new financial products that opened up new investment possibilities (while most previous crises were the consequence of overinvestment in new physical investment possibilities). Second, the global dimension of the current crisis is due to the increased connectivity of our already highly interconnected financial system. Both aspects have been largely ignored by academic economics. Research on the origin of instabilities, overinvestment and subsequent slumps has been considered as an exotic side track from the academic research agenda (and the curriculum of most economics programs).This, of course, was because it was incompatible with the premise of the rational representative agent. This paradigm also made economics blind with respect to the role of interactions and connections between actors (such as the changes in the network structure of the financial industry brought about by deregulation and introduction of new structured products). Indeed, much of the work on contagion and herding behavior (see Banerjee, 1992, and Chamley, 2002) which is closely connected to the network structure of the economy has not been incorporated into macroeconomic analysis.
We believe that economics has been trapped in a sub-optimal equilibrium in which much of its research efforts are not directed towards the most prevalent needs of society. Paradoxically self-reinforcing feedback effects within the profession may have led to the dominance of a paradigm that has no solid methodological basis and whose empirical performance is, to say the least, modest. Defining away the most prevalent economic problems of modern economies and failing to communicate the limitations and assumptions of its popular models, the economics profession bears some responsibility for the current crisis. It has failed in its duty to society to provide as much insight as possible into the workings of the economy and in providing warnings about the tools it created. It has also been reluctant to emphasize the limitations of its analysis. We believe that the failure to even envisage the current problems of the worldwide financial system and the inability of standard macro and finance models to provide any insight into ongoing events make a strong case for a major reorientation in these areas and a reconsideration of their basic premises.
Wouldn't matter if they did - while academics may do much of the research the people using it are unlikely to listen. And their bosses definitely won't. They want an answer and, no matter what caveats the quant doing the work provides, they'll ignore them (or argue that the probability of X happening, whatever X may be, is so small as to be meaningless). Or worse yet they'll argue that they understand the risks and uncertainties involved in the use of the model (who knows, maybe they do) and make the "business decision" to proceed anyways.
No argument but you're still talking about academics who form a (generally) distinct group from the people that use the models.
Beware of Geeks Bearing Formulas
Well worth the read.
http://www.nytimes.com/2009/03/10/science/10quant.html?ref=business