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.