Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels
Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Contents
The support vector regression (SVR) is a supervised machine learning technique that has been successfully employed to forecast financial volatility. As the SVR is a kernel-based technique, the choice of the kernel has a great impact on its forecasting accuracy. Empirical results show that SVRs with hybrid kernels tend to beat single-kernel models i...
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Full title
Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels
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Record Identifier
TN_cdi_proquest_miscellaneous_1893900477
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_1893900477
Other Identifiers
ISSN
1619-697X
E-ISSN
1619-6988
DOI
10.1007/s10287-016-0267-0
