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

Listed author(s):
  • 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. 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 based on 22 of Standard & Poor’s 500 industry group indices over the period 1995-2003. We find strong evidence in support of ‘thick’ modelling proposed in the forecasting literature by Granger and Jeon (2004).

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

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