A Rotational Invariant Technique for Rare Event Simulation
نویسندگان
چکیده
This paper introduces a new importance sampling technique to increase the efficiency of rare event simulation. By using a multidimensional rotational invariant auxiliary density, this method can be applied to estimate risk measures for credit risk portfolios where the method of importance sampling via mean shifting is not suitable. The loss distribution of the portfolio is obtained by simulating scenarios that are constituted by macroeconomic factors. Especially in inhomogeneous portfolios, i.e. portfolios with multiple areas of factors resulting in high losses, rotational invariant importance sampling is advantageous. Furthermore, rotational invariant importance sampling allows to calculate contributions to risk measures of different customer clusters in credit portfolios quicker and more precisely. The new method is applied to a real world credit portfolio with a high number of obligors. A comparison between standard Monte Carlo simulation and simulation using rotational invariant importance sampling in terms of computation time is given.
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