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In this paper we introduce a novel approach to risk estimation based onnonlinear factor models - the "StressVaR" (SVaR). Developed to evaluate therisk of hedge funds, the SVaR appears to be applicable to a wide range ofinvestments. Its principle is to use the fairly short and sparse history of thehedge fund returns to identify relevant risk factors among a very broad set ofpossible risk sources. This risk profile is obtained by calibrating acollection of nonlinear single-factor models as opposed to a singlemulti-factor model. We then use the risk profile and the very long and richhistory of the factors to asses the possible impact of known past crises on thefunds, unveiling their hidden risks and so called "black swans".  In backtests using data of 1060 hedge funds we demonstrate that the SVaR hasbetter or comparable properties than several common VaR measures - shows lessVaR exceptions and, perhaps even more importantly, in case of an exception, bysmaller amounts.  The ultimate test of the StressVaR however, is in its usage as a fundallocating tool. By simulating a realistic investment in a portfolio of hedgefunds, we show that the portfolio constructed using the StressVaR on averageoutperforms both the market and the portfolios constructed using common VaRmeasures.  For the period from Feb. 2003 to June 2009, the StressVaR constructedportfolio outperforms the market by about 6% annually, and on average thecompeting VaR measures by around 3%. The performance numbers from Aug. 2007 toJune 2009 are even more impressive. The SVaR portfolio outperforms the marketby 20%, and the best competing measure by 4%.

The StressVaR: A New Risk Concept for Superior Fund Allocation