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A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

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  • Leandro Maciel

    (Universidade Estadual de Campinas)

Abstract

Forecasting stock market returns volatility is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of fuzzy inference systems and GJR-GARCH modeling approach in order to consider the principles of time-varying volatility, leverage effects and volatility clustering, in which changes are cataloged by similarity. Moreover, a differential evolution (DE) algorithm is suggested to solve the problem of Fuzzy GJR-GARCH parameters estimation. The results indicate that the proposed method offers significant improvements in volatility forecasting performance in comparison with GARCH-type models and with a current Fuzzy-GARCH model reported in the literature. Furthermore, the DE-based algorithm aims to achieve an optimal solution with a rapid convergence rate.

Suggested Citation

  • Leandro Maciel, 2012. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Brazilian Review of Finance, Brazilian Society of Finance, vol. 10(3), pages 337-367.
    • Handle: RePEc:brf:journl:v:10:y:2012:i:3:p:337-367
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    References listed on IDEAS

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    1. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Ng, Hock Guan & McAleer, Michael, 2004. "Recursive modelling of symmetric and asymmetric volatility in the presence of extreme observations," International Journal of Forecasting, Elsevier, vol. 20(1), pages 115-129.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    6. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    7. Han, Heejoon & Park, Joon Y., 2008. "Time series properties of ARCH processes with persistent covariates," Journal of Econometrics, Elsevier, vol. 146(2), pages 275-292, October.
    8. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    9. Kapetanios, George & Labhard, Vincent & Price, Simon, 2006. "Forecasting using predictive likelihood model averaging," Economics Letters, Elsevier, vol. 91(3), pages 373-379, June.
    10. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
    13. Racine, Jeffrey, 2001. "On the Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 380-382, July.
    14. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    15. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    16. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    17. Lin, Edward M.H. & Chen, Cathy W.S. & Gerlach, Richard, 2012. "Forecasting volatility with asymmetric smooth transition dynamic range models," International Journal of Forecasting, Elsevier, vol. 28(2), pages 384-399.
    18. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    19. MArcelo Carvalho & MArco Aurelio Freire & Marcelo Cunha Medeiros & Leonardo Souza, 2006. "Modeling and forecasting the volatility of Brazilian asset returns," Textos para discussão 530, Department of Economics PUC-Rio (Brazil).
    20. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.

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    More about this item

    Keywords

    Volatility; GARCH models; Fuzzy Systems; Differential Evolution;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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