Incorporating higher moments into value‐at‐risk forecasting

Incorporating higher moments into value‐at‐risk forecasting

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Article ID: iaor20106119
Volume: 29
Issue: 6
Start Page Number: 523
End Page Number: 535
Publication Date: Sep 2010
Journal: Journal of Forecasting
Authors: ,
Keywords: value at risk
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.

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