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Portfolio risk assessment using multivariate extreme value methods

Hilal, Sawsan and Poon, Ser-Huang and Tawn, Jonathan (2014) Portfolio risk assessment using multivariate extreme value methods. Extremes, 17 (4). pp. 531-556. ISSN 1386-1999

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Abstract

This paper presents a model for the joint distribution of a portfolio by inferring extreme movements in financial markets. The core of our proposal is a statistical model for the tail of the joint distribution that attempts to capture accurately the data generating process through an extremal modelling for the univariate margins and for the multivariate dependence structure. The model addresses several features of financial returns by encompassing methods from both econometrics and extreme value theory, and hence, taking into account the asymmetric behaviour of extreme negative and positive returns, and the heterogeneous temporal as well as cross-sectional lead-lag extremal dependencies among portfolio constituents. The model facilitates scenario generation for future returns through extrapolation beyond the empirical observations upon which portfolio risk assessment is based. We provide empirical evidence for the proposed model by an application to stock market returns for the G5 economies.

Item Type: Article
Journal or Publication Title: Extremes
Uncontrolled Keywords: ARMA-GARCH filtering ; Asymptotic dependence ; Asymptotic independence ; Copula ; Multivariate extreme values ; 62G32 (Statistics of extreme values and tail inference) ; 62HXX (Multivariate analysis) ; 97M30 (Financial and insurance mathematics)
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 73876
Deposited By: ep_importer_pure
Deposited On: 18 Jun 2015 06:55
Refereed?: Yes
Published?: Published
Last Modified: 21 Oct 2017 05:10
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/73876

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