Abstract 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-dimensionalmarket 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.