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Support vector machine as an efficient framework for stock market volatility forecasting. (English) Zbl 1142.91718
Summary: Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data. Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models. SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data.
91B84Economic time series analysis
62M10Time series, auto-correlation, regression, etc. (statistics)
62P05Applications of statistics to actuarial sciences and financial mathematics
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