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