Fatter Tails

At the height of panic–and consternation–over turmoil in the financial sector at the turn of the year, many an accusatory finger was pointed at the risk analysis models the finance industry used to establish the value of various types of debts. Often the particular charge was that the simulations produced by Monte Carlo software lacked not only precision but even the capacity for precision. At the time, I responded in this blog that a risk assessment model is only as reliable as the probabilities it is build on.  

 
Now some folks in financial planning firms whose customers experienced the unhappy results of models created with their companies’ proprietary Monte Carlo software are becoming believers.  They are revisiting the probability distributions that generated those risk simulations, and in comments to the press are citing the need for distributions with fatter tails in order to account for randomness over longer time periods, in order to foresee a Black Swan event. This is no doubt due to the sudden prominence of author Nasim Nicholas Taleb, whose recent fame is due to his timely second-guessing of the markets.  
 
That people who work in finance and investing should start listening to a critic only after coming to grief is not surprising. What I do find surprising, however, is the planners who mention to reporters that they have been limited to the standard bell curve distribution or that their software doesn’t provide for sensitivity analyses. Any number of commercially available Monte Carlo software packages have been offering a fairly wide choice of distributions, along with sensitivity analyses for quite a while. Fatter tails aren’t hard to come by, so why can’t planners seem to find them?

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