Computational Management Science

, Volume 3, Issue 2, pp 147–160 | Cite as

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

  • Valeriy V. GavrishchakaEmail author
  • Supriya Banerjee
Original Paper


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.


Support Vector Machine Support Vector Regression Support Vector Machine Model GARCH Model Market Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Science Applications International CorporationMcLeanUSA

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