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

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  • M. Hashem Pesaran
  • Paolo Zaffaroni

Abstract

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 CESifo Group Munich in its series CESifo Working Paper Series with number 1358.

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Date of creation: 2004
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Handle: RePEc:ces:ceswps:_1358

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Keywords: model averaging; value-at-risk; decision based evaluation;

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Citations

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Cited by:
  1. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
  2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Practical volatility and correlation modeling for financial market risk management," CFS Working Paper Series 2005/02, Center for Financial Studies (CFS).
  3. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-46, October.
  4. Alessandra Amendola & Giuseppe Storti, 2009. "Combination of multivariate volatility forecasts," SFB 649 Discussion Papers SFB649DP2009-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representation of the UK Output Gap in the Presence of Trend Uncertainty," Birkbeck Working Papers in Economics and Finance, Birkbeck, Department of Economics, Mathematics & Statistics 0618, Birkbeck, Department of Economics, Mathematics & Statistics.
  6. Pesaran, Bahram & Pesaran, M. Hashem, 2007. "Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," IZA Discussion Papers 2906, Institute for the Study of Labor (IZA).
  7. M. Hashem Pesaran & Bahram Pesaran, 2007. "Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," CESifo Working Paper Series 2056, CESifo Group Munich.
  8. Garratt, Anthony & Lee, Kevin & Mise, Emi & Shields, Kalvinder, 2009. "Real time representation of the UK output gap in the presence of model uncertainty," International Journal of Forecasting, Elsevier, Elsevier, vol. 25(1), pages 81-102.
  9. Chua, Chew Lian & Suardi, Sandy & Tsiaplias, Sarantis, 2013. "Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(3), pages 442-455.

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