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Practical Volatility Modeling for Financial Market Risk Management

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  • Shamiri, Ahmed
  • Shaari, Abu Hassan
  • Isa, Zaidi

Abstract

Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in determining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 9790.

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Date of creation: 20 Aug 2007
Date of revision: 15 May 2008
Handle: RePEc:pra:mprapa:9790

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Keywords: Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models;

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  1. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, Econometric Society, vol. 68(5), pages 1097-1126, September.
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  3. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
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  8. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 363-90, March.
  9. Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.
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  11. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers, Rutgers University, Department of Economics 200314, Rutgers University, Department of Economics.
  12. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  13. Clements, Michael P. & Smith, Jeremy, 2002. "Evaluating multivariate forecast densities: a comparison of two approaches," International Journal of Forecasting, Elsevier, Elsevier, vol. 18(3), pages 397-407.
  14. Corradi, Valentina & Swanson, Norman R., 2005. "A Test For Comparing Multiple Misspecified Conditional Interval Models," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(05), pages 991-1016, October.
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