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Incorporating higher moments into value-at-risk forecasting

Author Info

Listed author(s):
  • Arnold Polanski

    (Queen's University Management School, Belfast, UK)

  • Evarist Stoja

    (School of Economics, Finance and Management, University of Bristol, UK)

Registered author(s):

    Abstract

    Value-at-risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time-varying higher-moments models. Copyright © 2009 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.1155
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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 29 (2010)
    Issue (Month): 6 ()
    Pages: 523-535

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    Handle: RePEc:jof:jforec:v:29:y:2010:i:6:p:523-535
    DOI: 10.1002/for.1155
    Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:

    1. repec:spr:comaot:v:23:y:2017:i:3:d:10.1007_s10588-016-9231-3 is not listed on IDEAS
    2. Del Brio, Esther B. & Perote, Javier, 2012. "Gram–Charlier densities: Maximum likelihood versus the method of moments," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 531-537.
    3. Olivier Bos & Béatrice Roussillon & Paul Schweinzer, 2013. "Agreeing on Efficient Emissions Reduction," CESifo Working Paper Series 4345, CESifo Group Munich.
    4. Abad, Pilar & Benito, Sonia, 2013. "A detailed comparison of value at risk estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 258-276.
    5. Pilar Abad Romero & Sonia Benito Muela & Miguel Angel Sánchez Granero & Carmen López, 2013. "Evaluating the performance of the skewed distributions to forecast Value at Risk in the Global Financial Crisis," Documentos de Trabajo del ICAE 2013-40, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    6. Lin, Shin-Hung & Huang, Hung-Hsi & Li, Sheng-Han, 2015. "Option pricing under truncated Gram–Charlier expansion," The North American Journal of Economics and Finance, Elsevier, vol. 32(C), pages 77-97.
    7. Andrés Mora-Valencia & Trino-Manuel Ñíguez & Javier Perote, 2017. "Multivariate approximations to portfolio return distribution," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 347-361, September.

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