A comparison of non-Gaussian VaR estimation and portfolio construction techniques

David Allen, Colin Lizieri and Stephen Satchell

Journal of Empirical Finance, 2020, vol. 58, issue C, 356-368

Abstract: We propose a multivariate model of returns that accounts for four of the stylised facts of financial data: heavy tails, skew, volatility clustering, and asymmetric dependence with the aim of improving the accuracy of risk estimates and increasing out-of-sample utility of investors’ portfolios. We accommodate volatility clustering, the generalised Pareto distribution to capture heavy tails and skew, and the skewed-t copula to provide for asymmetric dependence. The proposed approach produces more accurate VaR estimates than seven competing approaches across eight data sets encompassing five asset classes. We show that this produces portfolios with higher utility, and lower downside risk than alternative approaches including mean–variance. We confirm that investors can substantially increase utility by accounting for departures from normality.

Keywords: VaR; Tail-risk; Skewed-t copula; Portfolio optimisation; Risk management (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:58:y:2020:i:c:p:356-368

DOI: 10.1016/j.jempfin.2020.07.001

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