Nonparametric Estimation of Value-at-Risk

XARXA de Referencia en Economia Aplicada, XREAP2012-19

40 Pages Posted: 17 Oct 2012

See all articles by Ramon Alemany

Ramon Alemany

University of Barcelona

Catalina Bolancé

University of Barcelona - Department of Econometrics

Montserrat Guillen

Date Written: October 16, 2012

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

Suggested Citation

Alemany, Ramon and Bolancé, Catalina and Guillen, Montserrat, Nonparametric Estimation of Value-at-Risk (October 16, 2012). XARXA de Referencia en Economia Aplicada, XREAP2012-19, Available at SSRN: https://ssrn.com/abstract=2162585 or http://dx.doi.org/10.2139/ssrn.2162585

Ramon Alemany

University of Barcelona ( email )

Department of Econometrics
Av. Diagonal, 690
Barcelona, Barcelona 08034
Spain

Catalina Bolancé (Contact Author)

University of Barcelona - Department of Econometrics ( email )

Av. Diagonal 690
Barcelona, E-08034
Spain

No contact information is available for Montserrat Guillen

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