The Perils of Quantitative Financial Modeling
Observations from a panel discussion on the subject.
The title of the program was benignly inaccurate as relatively little time was spent discussing the future of quantitative finance. Rather, the discussion was much more directed to the inherent flaws in models employed in quantitative financial analysis and why in the early summer of 2007 so very many "Quants" failed to see that the wave was cresting. I found the subject actually discussed much more interesting than I believe discussion of the promoted subject would have been.
The panel discussion was moderated by Dr. Ann H. Tucker, Professor of Applied Mathematics and Statics at Stony Brook University.
There were three panel members: Dr. Robert Frey, President and CEO of Harbor Financial Management and a Professor of Applied Mathematics and Statics at Stony Brook; Dr. Andrew Mallhaupt, a past Director of Research of the Meridian Group at S.A.C. Capital Management and who also served at Renaissance Technologies; and Andrew Lapkin, MBA, President and COO of Measurisk, who previously was in charge of developing Bankers Trust's comprehensive market risk management system.
I preface my comments with the observation that I'm not a Quant and have no training or experience in quantitative analysis. Before attending this panel discussion, I couldn't have told you the difference between a "Minsky Moment" and Minsky's Burlesque. (More on the former below; as to the latter, if you don't know, I refer you to the movie, The Night They Raided Minsky's.) I attended the panel discussion because I'm a Stony Brook University Alum (1964), have served on the Board of Trustees of the Stony Brook Foundation with Dr. Frey for many years, and, since the commencement of my tenure as General Counsel of Minyanville, have rekindled an earlier interest in macroeconomics.
For the benefit of any neophytes in the audience (Dr. Frey looked directly at me as he spoke), the first subject addressed by the panel was the definition of quantitative financial analysis. Simply put, quantitative financial analysis is a predictive exercise that measures risk and which is based on evidence. A large part of that evidence is historical -- what can be gleaned regarding future market performance by examining what's occurred in the past -- and using that information as input to a model into which current information is also fed. "Reasonable" assumptions are also included as input.
The various financial firms have their respective models, and there are also outside firms such as Andrew Larkin's Measurisk, that employ their respective models. Each of these entities, in addition to the input and assumptions previously mentioned, employs its own "secret sauce," algorithms attempting to find correlations among the various pieces of data. Each firm's algorithms are believed by the firm to yield results that hopefully will be more accurate than those of its competitors.
In 2006, if one were evaluating the future performance of Collateralized Debt Obligations based on ordinary 30-year mortgages, for example, one would presumably input historical data regarding foreclosure rates at various interest levels, unemployment rates, real earnings rates, rates of inflation, GDP increases or decreases, etc. Current data would also be added together with assumptions deemed to be reasonable, e.g. that the foreclosure rate of 30 year mortgages has never exceeded X% over the past Y years would be unlikely to exceed X% in the foreseeable future.
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