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- W2012421215 abstract "This paper presents a new heuristic for fast approximation of VaR (Value-at-Risk) and CVaR (conditional Value-at-Risk) for financial portfolios where the net worth of portfolio is a nonlinear function of possibly non-Gaussian risk factors. The proposed method is based on mapping non-normal marginal distributions into normal distributions via a probability conserving transformation and then using a quadratic, i.e. Delta-Gamma approximation for the portfolio value. The method is extremely general and can deal with a wide range of marginal distributions of risk factors, including non-parametric distributions. Its computational load is comparable with Delta-Gamma-Normal method based on Fourier inversion. However, unlike the Delta-Gamma-Normal method, the proposed heuristic preserves the tail behavior of the individual risk factors, which may be seen as a significant advantage. We demonstrate the utility of the new method with comprehensive numerical experiments on simulated as well as real financial data." @default.
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- W2012421215 title "Measuring the risk of a non-linear portfolio with fat-tailed risk factors through a probability conserving transformation" @default.
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- W2012421215 doi "https://doi.org/10.1093/imaman/dpu015" @default.
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