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Incorporating higher moments into value-at-risk forecasting

  • Arnold Polanski

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

  • Evarist Stoja

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

Registered author(s):

    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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