Support vector machine as an efficient framework for stock market volatility forecasting

Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. A support vector machine (SVM) has been proposed as a complementary 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 mainstream 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.