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Model Averaging in Risk Management with an Application to Futures Markets

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Author Info

  • Pesaran, M.H.
  • Schleicher, C.
  • Zaffaroni, P.

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. 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 ¯nite-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.

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File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe0808.pdf
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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0808.

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Length: 24
Date of creation: Jan 2008
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Handle: RePEc:cam:camdae:0808

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Web page: http://www.econ.cam.ac.uk/index.htm

Related research

Keywords: Model Averaging; Value-at-Risk; Decision Based Evaluations.;

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References

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Citations

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Cited by:
  1. Michael McAleer, 2009. "The Ten Commandments for Optimizing Value-at-Risk and Daily Capital Charges," CIRJE F-Series CIRJE-F-652, CIRJE, Faculty of Economics, University of Tokyo.
  2. 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.
  3. 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.
  4. 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.
  5. Katrin Assenmacher-Wesche & M. Hashem Pesaran, 2007. "Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows," CESifo Working Paper Series 2116, CESifo Group Munich.
  6. 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.
  7. Enrique Moral-Benito, 2011. "Model averaging in economics," Banco de Espa�a Working Papers 1123, Banco de Espa�a.
  8. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
  9. González-Rivera, Gloria & Yoldas, Emre, 2012. "Autocontour-based evaluation of multivariate predictive densities," International Journal of Forecasting, Elsevier, vol. 28(2), pages 328-342.
  10. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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