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Varying the VaR for unconditional and conditional environments
Author(s)
Date Issued
2004
Date Available
26T13:49:45Z May 2009
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.
Type of Material
Working Paper
Publisher
University College Dublin. School of Business. Centre for Financial Markets
Series
Centre for Financial Markets working paper series
WP-04-06
Copyright (Published Version)
Centre for Financial Markets, 2004
Classification
G1
G10
Subject – LCSH
Risk--Econometric models
Extreme value theory
Econometric models
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
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