Can we use kernel smoothing to estimate Value at Risk and Tail Value at Risk?
نویسندگان
چکیده
In this paper we analyse nonparametric methods to estimate risk measures in loss distributions. We study kernel estimation for Value-at-Risk and Tail Value-at-Risk based on transformation of the original data. The proposed method consists of a double transformation kernel estimation. We show that a suitable bandwidth selection criterion has a direct expression for the optimal smoothing parameter. The bandwidth can accommodate to the given extreme quantile level and expected shortfall. The procedure is useful for large data sets frequently available in finance and insurance.
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