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Incorporating Higher Moments into Value-at-Risk Forecasting

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)523 - 535
Number of pages13
JournalJournal of Forecasting
Volume29
Issue number6
DOIs
DatePublished - Sep 2010

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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