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Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting

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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.

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Correspondence to Valeriy V. Gavrishchaka.

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Gavrishchaka, V.V., Banerjee, S. Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting. CMS 3, 147–160 (2006).

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