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.
關聯:
Journal of Intelligent and Fuzzy Systems, vol. 31, no. 3, pp. 1987-2000
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