Practical Volatility Modeling for Financial Market Risk Management

Shamiri, Ahmed and Shaari, Abu Hassan and Isa, Zaidi (2007): Practical Volatility Modeling for Financial Market Risk Management.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models
• Subjects: D - Microeconomics > D5 - General Equilibrium and Disequilibrium > D53 - Financial Markets; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; ; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection

References

Bai, J. (2003), “Testing parametric conditional distributions of dynamic models”, Review of Economics and Statistics, Vol. 85 No.3, pp. 531-549.

Bao, Y., Lee, T. H. and Saltoglu, B. (2006), “Comparing density forecast models”, Journal of Forecasting, Vol. 26 No. 3, pp. 203-225.

Berkowitz, J. (2001), “Testing density forecasts with applications to risk management”, Journal of Business and Economic Statistics, Vol. 19 No.4, pp. 465-474.

Christoffersen, P. F. (1998), “Evaluating interval forecasts”, International Economic Review, Vol. 39 No.4, pp. 841-862.

Clements, M. P. and Smith, J. (2000), “Evaluating the forecast densities of linear and non-linear models: applications to output growth and unemployment”, Journal of Forecasting, Vol. 19 No.4, pp. 225-276.

Clements, M. P. and Smith, J. (2002), “Evaluating multivariate forecast densities: a comparison of two approaches”, International Journal of Forecasting, Vol. 18 No.3, pp. 397-407.

Corradi, V. and Swanson, N. R. (2005), “A test for comparing multiple misspecified conditional interval models”, Econometric Theory, Vol. 21 No.5, pp. 991-1016.

Diebold, F. X., Gunther, T. A. and Tay, A. S. (1998a), “Evaluating density forecasts with applications to financial risk management”, International Economic Review, Vol. 39 No.4, pp. 863-883.

Diebold, F. X., Hahn, J. and Tay, A. S. (1999), “Multivariate density forecast evaluation and calibration in financial risk management: High-frequency returns on foreign exchange”, The Review of Economics and Statistics, Vol. 81 No.4, pp. 661-673.

Diebold, F. X., Tay, A. S. and Wallis, K. F. (1998b), “Evaluating density forecasts of inflation: The survey of professional forecasters”, in Engle R., White H. (Ed.), Festschrift in honor of C.W. J. Granger, Oxford University Press, Oxford, pp. 76–90.

Gallant, A. R. and Nychka, D. W. (1987), “Semi-nonparametric maximum likelihood estimation”, Econometrica, Vol. 55 No.2, pp. 363-390.

Hansen, P. R. (2001), “An unbiased and powerful test for superior predictive ability”, working paper [01-06], Department of Economics, Brown University, Providence, Jun.

Hassan, A. and Shamiri, A. (2007), “Modeling and forecasting volatility of the Malaysian and Singaporean stock indices using asymmetric GARCH models and non-normal densities”, Malaysian Journal of Mathematical Science, Vol. 1No.1, pp.83-102.

Poon, S. H. and Granger, C. W. J. (2003), “Forecasting volatility in financial markets: A review”, Journal of Economic Literature, Vol. 41 No.2, pp. 478-539.

Rosenblatt, M. (1952), “Remarks on a multivariate transformation”, The Annals of Mathematical Statistics, Vol. 23 No.3, pp. 470-472.

Tay, A. S. and Wallis, K. F. (2000), “Density forecasting: a survey”, Journal of Forecasting, Vol. 19 No.4, pp. 235-254.

White, H. (2000), “A reality check for data snooping”, Econometrica, Vol. 68 No.5, pp. 1097-1126.