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Practical Volatility Modeling for Financial Market Risk Management

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Author Info
Shamiri, Ahmed
Shaari, Abu Hassan
Isa, Zaidi

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

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Find related papers by JEL classification:
D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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  1. 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, vol. 39(4), pages 863-83, November.
  2. Clements, M.P. & Smith J., 1998. "Evaluating The Forecast of Densities of Linear and Non-Linear Models: Applications to Output Growth and Unemployment," The Warwick Economics Research Paper Series (TWERPS) 509, University of Warwick, Department of Economics.
  3. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
  4. 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.
  5. Jushan Bai, 2003. "Testing Parametric Conditional Distributions of Dynamic Models," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 531-549, 06. [Downloadable!] (restricted)
  6. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1998. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," Working Papers 98-15, New York University, Leonard N. Stern School of Business, Department of Economics.
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  7. Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics. [Downloadable!]
  8. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  9. Corradi, Valentina & Swanson, Norman R., 2005. "A Test For Comparing Multiple Misspecified Conditional Interval Models," Econometric Theory, Cambridge University Press, vol. 21(05), pages 991-1016, October. [Downloadable!]
  10. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society. [Downloadable!]
  11. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  12. Clements, Michael P. & Smith, Jeremy, 2002. "Evaluating multivariate forecast densities: a comparison of two approaches," International Journal of Forecasting, Elsevier, vol. 18(3), pages 397-407. [Downloadable!] (restricted)
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