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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.

Suggested Citation

  • Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2007. "Practical Volatility Modeling for Financial Market Risk Management," MPRA Paper 9790, University Library of Munich, Germany, revised 15 May 2008.
    • Handle: RePEc:pra:mprapa:9790
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    References listed on IDEAS

    as
    1. Clements, Michael P. & Smith, Jeremy, 1998. "Evaluating The Forecast Densities Of Linear And Non-Linear Models: Applications To Output Growth And Unemployment," Economic Research Papers 268791, University of Warwick - Department of Economics.
    2. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    3. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1997. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," NBER Working Papers 6228, National Bureau of Economic Research, Inc.
    4. 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 841-862, November.
    5. Peter Reinhard Hansen, 2001. "An Unbiased and Powerful Test for Superior Predictive Ability," Working Papers 2001-06, Brown University, Department of Economics.
    6. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    7. 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.
    8. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    9. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1999. "Multivariate Density Forecast Evaluation And Calibration In Financial Risk Management: High-Frequency Returns On Foreign Exchange," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 661-673, November.
    10. 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 863-883, November.
    11. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    12. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    13. 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.
    14. Corradi, Valentina & Swanson, Norman R., 2005. "A Test For Comparing Multiple Misspecified Conditional Interval Models," Econometric Theory, Cambridge University Press, vol. 21(5), pages 991-1016, October.
    15. Jushan Bai, 2003. "Testing Parametric Conditional Distributions of Dynamic Models," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 531-549, August.
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    More about this item

    Keywords

    Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models;
    All these keywords.

    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; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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