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Nonparametric estimation of Value-at-Risk

Author

Listed:
  • 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)

  • Montserrat Guillén

    (Department of Econometrics, Riskcenter-IREA, University of Barcelona,Av. Diagonal, 690, 08034 Barcelona, Spain)

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.

Suggested Citation

  • Ramon Alemany & Catalina Bolancé & Montserrat Guillén, 2012. "Nonparametric estimation of Value-at-Risk," Working Papers XREAP2012-19, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2012.
    • Handle: RePEc:xrp:wpaper:xreap2012-19
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    References listed on IDEAS

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    Cited by:

    1. Alemany, Ramon & Bolancé, Catalina & Guillén, Montserrat, 2013. "A nonparametric approach to calculating value-at-risk," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 255-262.
    2. Gurrola-Perez, Pedro & Murphy, David, 2015. "Filtered historical simulation Value-at-Risk models and their competitors," Bank of England working papers 525, Bank of England.

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    Keywords

    kernel estimation; bandwidth selection; quantile; risk measures..;
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