Alan Greenspan recognizes in clear terms one of the fundamental problems with our current financial system, as enunciated in the title of his oped: We will never have a perfect model of risk

The essential problem is that our models – both risk models and econometric models – as complex as they have become, are still too simple to capture the full array of governing variables that drive global economic reality. A model, of necessity, is an abstraction from the full detail of the real world. In line with the time-honoured observation that diversification lowers risk, computers crunched reams of historical data in quest of negative correlations between prices of tradeable assets; correlations that could help insulate investment portfolios from the broad swings in an economy. When such asset prices, rather than offsetting each other’s movements, fell in unison on and following August 9 last year, huge losses across virtually all risk-asset classes ensued.

The most credible explanation of why risk management based on state-of-the-art statistical models can perform so poorly is that the underlying data used to estimate a model’s structure are drawn generally from both periods of euphoria and periods of fear, that is, from regimes with importantly different dynamics.

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But these models do not fully capture what I believe has been, to date, only a peripheral addendum to business-cycle and financial modelling – the innate human responses that result in swings between euphoria and fear that repeat themselves generation after generation with little evidence of a learning curve.

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Anticipated events are arbitraged away. But if, as I strongly suspect, periods of euphoria are very difficult to suppress as they build, they will not collapse until the speculative fever breaks on its own. Paradoxically, to the extent risk management succeeds in identifying such episodes, it can prolong and enlarge the period of euphoria. But risk management can never reach perfection. It will eventually fail and a disturbing reality will be laid bare, prompting an unexpected and sharp discontinuous response.

This sentiment is similar to that which motivates my distaste for “quant funds” or other overly complex sets of financial analyses. When building a house the foundation is important, but it can be somewhat imperfect and the house still stands. But when building a tower, one had better be certain that the foundation is secure.

The elegantly complex models of correlations and statistical almost-truths piled upon estimations of human behavior work in times of human consistency. But when people change their minds, or behave in innovative, stupid, or perhaps just rationally greedy ways, it is sometimes hard to predict the outcomes. And as we are seeing, being wrong with a multi-Trillion dollar tower of financial assets on top of our assumptions produces dislocations that can be somewhat painful.