Resource title

Model Averaging in Risk Management with an Application to Futures Markets

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Resource description

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as average models. The asymptotic as well as the exact finite-sample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991-2007. The empirical evidence supports the use of thick model averaging strategies over single models or Bayesian type model averaging procedures.

Resource author

Mohammad Hashem Pesaran, Christoph Schleicher, Paolo Zaffaroni

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Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/26276

Resource license

Adapt according to the presented license agreement and reference the original author.