Varying the VaR for unconditional and conditional environments

By John Cotter. 2007

47 citations recorded by the source corpus.

Abstract Accurate forecasting of risk is the key to successful risk management techniques. Using the largest stock index futures from 12 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 accounting for volatility clustering 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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/7a09b985cfc879f7769a678683d2b1c317d37ad9

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In Economics and Econometrics

1. Marco Rocco
2. M. Nieto
3. Esther Ruiz

In Finance

1. Marco Rocco
2. M. Nieto
3. Esther Ruiz

Citations in the corpus

• Extreme Value Theory for Finance: A Survey (2012, methodology): “Further applications can be found in Bali (2007), Bali and Neftci (2003), Byström (2004, 2005), Cotter (2007a, 2007b), Krehbiel and Adkins (2005), Kuester et al. (2006), Lestano and Jacobs (2007).”