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Model averaging in risk management with an application to futures markets

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  • Pesaran, M. Hashem
  • Schleicher, Christoph
  • Zaffaroni, Paolo

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 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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 16 (2009)
Issue (Month): 2 (March)
Pages: 280-305

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Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:280-305

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Web page: http://www.elsevier.com/locate/jempfin

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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. Antonio Ciccone & Marek Jarocinski, 2010. "Determinants of Economic Growth. Will Data Tell?," Working Papers 1009, BBVA Bank, Economic Research Department.
  2. González-Rivera, Gloria & Yoldas, Emre, 2012. "Autocontour-based evaluation of multivariate predictive densities," International Journal of Forecasting, Elsevier, vol. 28(2), pages 328-342.
  3. Enrique Moral-Benito, 2011. "Model averaging in economics," Banco de Espa�a Working Papers 1123, Banco de Espa�a.
  4. 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.
  5. 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.
  6. Gradojevic, Nikola & Gençay, Ramazan, 2013. "Fuzzy logic, trading uncertainty and technical trading," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 578-586.
  7. Pesaran, M.H. & Assenmacher-Wesche, K., 2007. "Assessing forecast uncertainties in a VECX* model for Switzerland: an exercise in forecast combination across models and observation windows," Cambridge Working Papers in Economics 0746, Faculty of Economics, University of Cambridge.
  8. Bahram Pesaran & M. Hashem Pesaran, 2010. "Conditional Volatility and Correlations of Weekly Returns and the VaR Analysis of 2008 Stock Market Crash," CESifo Working Paper Series 3023, CESifo Group Munich.
  9. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2012. "Sequential Estimation Of Shape Parameters In Multivariate Dynamic Models," Working Papers wp2012_1201, CEMFI.
  10. Vanina Forget, 2012. "Doing well and doing good: a multi-dimensional puzzle," Working Papers hal-00672037, HAL.

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