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Model Averaging and Value-at-Risk based Evaluation of Large Multi Asset Volatility Models for Risk Management

  • Hashem Pesaran

    (University of Cambridge & USC)

  • Paolo Zaffaroni

    (University of Cambridge)

  • Banca d'Italia)

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. In particular, it is shown that under certain conditions portfolio returns based on an average model will be more fat-tailed than if based on an individual underlying model with the same average volatility. Evaluation of volatility models is also considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as ‘average’ models and its exact and asymptotic properties are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns based on twenty two of Standard & Poor’s 500 industry group indices over the period January 2, 1995 to October 13, 2003, inclusive.

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Paper provided by Money Macro and Finance Research Group in its series Money Macro and Finance (MMF) Research Group Conference 2004 with number 101.

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Date of creation: 17 Sep 2004
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Handle: RePEc:mmf:mmfc04:101
Contact details of provider: Web page: http://www.essex.ac.uk/afm/mmf/index.html

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