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Incorporating higher moments into value-at-risk forecasting

✍ Scribed by Arnold Polanski; Evarist Stoja

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
259 KB
Volume
29
Category
Article
ISSN
0277-6693

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✦ Synopsis

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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