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Managing portfolio risk using multivariate extreme value methods

This paper provides a strategy for portfolio risk management by inferring extreme movements in financial markets. The core of the provided strategy is a statistical model for the joint tail distribution that attempts to capture accurately the data generating process through an extremal modelling for the univariate margins and the multivariate dependence structure. It takes into account the asymmetric behavior of extreme negative and positive returns, the heterogeneous temporal and cross-sectional lead-lag extremal dependencies among the portfolio constituents. The strategy facilitates scenario generation for future returns, estimation of portfolio profit-and-loss distribution and calculation of risk measures, and hence, enabling us to answer several questions of economic interest. We illustrate the usefulness of our proposal by an application to stock market returns for the G5 economies.
Year of publication:
2013
Authors: Hilal, Sawson ; Poon, Ser-Huang ; Tawn, Jonathan
Publisher: Manchester : The University of Manchester, Manchester Business School
Subject: ARMA-GARCH filtering | Asymptotic dependence | Asymptotic independence | Copula | Multivariate extreme values
Series:
Type of publication: Book / Working Paper
Type of publication (narrower categories): Working Paper
Language: English
Other identifiers:
772534845 [GVK]
hdl:10419/102368 [Handle]
Classification: C14 - Semiparametric and Nonparametric Methods ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models
Source:
Persistent link: https://ebvufind01.dmz1.zbw.eu/10010409432
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