Fuzzy support vector regression model for forecasting stock market volatility

Stock market volatility exhibits characteristics such as clustering and time-varying fluctuations. This paper proposes a two-stage method for addressing these concerns. The involved procedure is as follows: First, a fuzzy system is used to analyze clustering regimes according to the size of fluctuations. Second, the clustering regimes of Stage I are used to establish a support vector regression (SVR) model, which is used to reduce the time-varying complexity. However, the fuzzy-SVR model combines the parameters of membership functions and SVR models, further complicating the problem. Thus, this paper presents parallel research based on a genetic algorithm (GA) for estimating the parameters of the membership functions and SVR model. Data from four stock markets-the Taiwan Stock Exchange weighted stock index (Taiwan), the NASDAQ Composite index, the Hang Seng index (Hong Kong), and the Shanghai Composite index (Shanghai)- were analyzed in this study to illustrate the performance of the proposed model. According to the simulation results, the forecasting of out-of-sample volatility performance was significantly improved when the model accounted for the behavioral effect of both clustering and time-varying fluctuations.