Nonparametric estimation of Value-at-Risk

Ramon Alemany (), Catalina Bolancé () and Montserrat Guillen
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Ramon Alemany: Department of Econometrics, Riskcenter-IREA, University of Barcelona,Av. Diagonal, 690, 08034 Barcelona, Spain
Catalina Bolancé: Department of Econometrics, Riskcenter-IREA, University of Barcelona,Av. Diagonal, 690, 08034 Barcelona, Spain

No XREAP2012-19, Working Papers from Xarxa de Referència en Economia Aplicada (XREAP)

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)
Pages: 40 pages
Date: 2012-10, Revised 2012-10
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (2)

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http://www.xreap.cat/RePEc/xrp/pdf/XREAP2012-19.pdf First version, 2012 (application/pdf)
http://www.xreap.cat/RePEc/xrp/pdf/XREAP2012-19.pdf Revised version, 2012 (application/pdf)

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