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

Leandro Maciel

Brazilian Review of Finance, 2012, vol. 10, issue 3, 337-367

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.

Keywords: Volatility; GARCH models; Fuzzy Systems; Differential Evolution (search for similar items in EconPapers)
JEL-codes: C53 C61 G17 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Chapter: A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting (2013)
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