文件名称:SupportVectorMachineasanEfficientFrameworkforStock
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- 2012-11-26
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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
fr a mework 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.
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
fr a mework 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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Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting.pdf