Varying the VaR for unconditional and conditional environments

John Cotter

Centre for Financial Markets Working Papers from Research Repository, University College Dublin

Abstract: Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from twelve European bourses, this paper presents VaR measures based on their unconditional and conditional distributions for single and multi-period settings. These measures underpinned by extreme value theory are statistically robust explicitly allowing for fat-tailed densities. Conditional tail estimates are obtained by adjusting the unconditional extreme value procedure with GARCH filtered returns. The conditional modelling results in iid returns allowing for the use of a simple and efficient multi-period extreme value scaling law.The paper examines the properties of these distinct conditional and unconditional trading models. The paper finds that the biases inherent in unconditional single and multi-period estimates assuming normality extend to the conditional setting.

Keywords: Value at Risk; GARCH filter; Extreme value theory; Conditional risk; Risk--Econometric models; Extreme value theory; Econometric models (search for similar items in EconPapers)
JEL-codes: G1 G10 (search for similar items in EconPapers)
Date: 2004
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http://hdl.handle.net/10197/1138 First version, 2004 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:rru:cfmwps:10197/1138

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