An Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting
نویسندگان
چکیده
Volatility forecasting is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes an evolving fuzzyGARCH approach to model and forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of evolving fuzzy systems and GARCH modeling approach in order to consider the principles of time-varying volatility and volatility clustering, in which changes are cataloged by similarity. Evolving fuzzy systems use data streams to continuously adapt the structure and functionality of fuzzy models to improve their performance, which is computationally efficient. The results show the high potential of the evolving fuzzy-GARCH model to forecast stock returns volatility, outperforming GARCH-type models in statistical terms.
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