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Combination of Value-at-Risk Models with Support Vector Machine
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 Title & Authors
Combination of Value-at-Risk Models with Support Vector Machine
Kim, Yong-Tae; Shim, Joo-Yong; Lee, Jang-Taek; Hwang, Chang-Ha;
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Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.
GARCH;historical simulation;model selection;quantile regression;support vector machine(SVM);Value-at-Risk;
 Cited by
Support Vector Quantile Regression with Weighted Quadratic Loss Function,Shim, Joo-Yong;Hwang, Chang-Ha;

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