Incorporating higher moments into value at risk forecasting

Polanski, Arnold ORCID: https://orcid.org/0000-0001-9146-6364 and Stoja, Evarist (2010) Incorporating higher moments into value at risk forecasting. Journal of Forecasting, 29 (6). pp. 523-535. ISSN 0277-6693

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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.

Item Type:	Article
Faculty \ School:	Faculty of Social Sciences > School of Economics
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Economic Theory Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance
Depositing User:	Julie Frith
Date Deposited:	09 Feb 2012 10:37
Last Modified:	15 Jun 2023 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/36955
DOI:	10.1002/for.1155

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