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- Title
A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting.
- Authors
Maciel, Leandro
- 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 DEbased algorithm aims to achieve an optimal solution with a rapid convergence rate.
- Subjects
BUSINESS forecasting; MARKET volatility; SECURITIES trading; ALGORITHMS; STOCHASTIC convergence
- Publication
Brazilian Review of Finance / Revista Brasileira de Finanças, 2012, Vol 10, Issue 3, p337
- ISSN
1679-0731
- Publication type
Article
- DOI
10.12660/rbfin.v10n3.2012.3871