Importance Sampling Enhanced with the COS Method for the Portfolio Risk Allocation
Fang Fang, Xiaoyu Shen, Qinling Wang
Abstract
We introduce ISCOS, a cross-entropy importance-sampling calibration method for rare credit-portfolio losses. We derive Gaussian and Gaussian--inverse-Gamma proposals and analyse the propagation of finite-COS approximation errors to the fitted parameters. Numerical experiments for Gaussian and Student t-copula credit portfolios show the efficiency of this method.
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