The Tail that Wags the Hedge Fund Dog (Slideshow Presentation)

This is a set of slides I am using at the Fall 2008 conference of the Journal of Investment Management in Boston. It illustrates my paper The Tail that Wags the Hedge Fund Dog, also available at SSRN: http://ssrn.com/abstract=1142844. Viewers of this slideshow should be sure to click the image on the third slide (labeled #2 since the cover slide is not numbered), to watch a 60-second movie of the burning house. The speaking point here is that we are going to consider a portfolio of hedge funds to be a portfolio of insurance policies against a set of risk factors, and we are going to explore not just the unconditional value of each insurance policy, but more importantly what they will pay us in the left tail of the risk factor distribution, or when the house burns down. Viewers should also click the image on the thirteenth slide (labeled #12, again because the title slide is not numbered), to watch a three minute animation of the evolution of the risk budget of a portfolio of hedge funds; once you click the chart, be patient - the animation will start after 10 or 15 seconds.Here is the abstract from my original paper:We consider a portfolio of hedge funds as a portfolio of insurance policies against a set of risk factors. We highlight some deficiencies of linear estimation procedures and apply several nonlinear approaches to an individual hedge fund and also to a set of investable hedge fund indices. We apply Extreme Value Theory to the estimation of hedge funds tail risk. We find that although some hedge fund indices may apparently be well-fit by short-, medium-, and long-tailed classes of Generalized Extreme Value distributions, in practice it is more conservative to use the longest-tailed class of GEV for which statistically significant goodness-of-fit may be attained. In particular we find that, examining the monthly returns of twelve HFRX investable hedge fund indexes over the ten-year period from January 1998 through December 2007, seven indexes are well-fit by long-tailed distributions including the generalized Pareto and the Cauchy, while five indexes are well-fit by the medium-tailed Gamma distribution. Care should be taken because seven of the twelve indexes also appear to be well-fit by the normal distribution, and we caution that for purposes of tail-risk estimation, acceptance of the normal distribution would prove illusory and hazardous