A nonparametric approach to calculating value-at-risk

Ramon Alemany, Catalina Bolancé and Montserrat Guillen

Insurance: Mathematics and Economics, 2013, vol. 52, issue 2, 255-262

Abstract: A method to estimate an extreme quantile that requires no distributional assumptions is presented. The approach is based on transformed kernel estimation of the cumulative distribution function (cdf). The proposed method consists of a double transformation kernel estimation. We derive optimal bandwidth selection methods that have a direct expression for the smoothing parameter. The bandwidth can accommodate to the given quantile level. The procedure is useful for large data sets and improves quantile estimation compared to other methods in heavy tailed distributions. Implementation is straightforward and R programs are available.

Keywords: Kernel estimation; Bandwidth selection; Quantile; Risk measures (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:52:y:2013:i:2:p:255-262

DOI: 10.1016/j.insmatheco.2012.12.008

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