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Model Averaging in Risk Management with an Application to Futures Markets


  • M. Hashem Pesaran
  • Christoph Schleicher
  • Paolo Zaffaroni


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.

Suggested Citation

  • M. Hashem Pesaran & Christoph Schleicher & Paolo Zaffaroni, 2008. "Model Averaging in Risk Management with an Application to Futures Markets," CESifo Working Paper Series 2231, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_2231

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    Cited by:

    1. Wang, Nanying & Houston, Jack E., 2015. "The Co-movement between Non-GM and GM Soybean Price in China: Evidence from China Futures Market," 2015 Conference, August 9-14, 2015, Milan, Italy 211914, International Association of Agricultural Economists.
    2. Antonio Ciccone & Marek Jarociński, 2010. "Determinants of Economic Growth: Will Data Tell?," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(4), pages 222-246, October.
    3. Nanying Wang & Jack E. Houston, 2016. "The Co-Movement between Non-GM and GM Soybean Prices in China: Evidence from Dalian Futures Market (2004-2014)," Applied Economics and Finance, Redfame publishing, vol. 3(4), pages 37-47, November.
    4. Pesaran, Bahram & Pesaran, M. Hashem, 2010. "Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash," Economic Modelling, Elsevier, vol. 27(6), pages 1398-1416, November.
    5. Wang, Nanying & Houston, Jack, 2015. "The Comovement between Non-GM and GM Soybean Price in China: Evidence from Dalian Futures Market," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196775, Southern Agricultural Economics Association.
    6. Valeria Bignozzi & Claudio Macci & Lea Petrella, 2017. "Large deviations for risk measures in finite mixture models," Papers 1710.03252,, revised Feb 2018.
    7. González-Rivera, Gloria & Yoldas, Emre, 2012. "Autocontour-based evaluation of multivariate predictive densities," International Journal of Forecasting, Elsevier, vol. 28(2), pages 328-342.
    8. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    9. Assenmacher-Wesche, Katrin & Pesaran, M. Hashem, 2007. "Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows," IZA Discussion Papers 3071, Institute for the Study of Labor (IZA).
    10. Michael McAleer, 2009. "The Ten Commandments For Optimizing Value-At-Risk And Daily Capital Charges," Journal of Economic Surveys, Wiley Blackwell, pages 831-849.
    11. Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2013. "Sequential estimation of shape parameters in multivariate dynamic models," Journal of Econometrics, Elsevier, vol. 177(2), pages 233-249.
    12. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
    13. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    14. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.
    15. Gloria Gonzalez-Rivera & Emre Yoldas, 2010. "Multivariate Autocontours for Specification Testing in Multivariate GARCH Models," Working Papers 201436, University of California at Riverside, Department of Economics.

    More about this item


    model averaging; Value-at-Risk; decision based evaluations;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions


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