A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting
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 t...
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| Format: | Article |
| Language: | English |
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Brazilian Society of Finance
2012-09-01
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| Series: | Revista Brasileira de Finanças |
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| Online Access: | http://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/view/3871 |
| Summary: | 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. |
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| ISSN: | 1679-0731 1984-5146 |