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# Forecasting volatility based on wavelet support vector machine

@article{Tang2009ForecastingVB, title={Forecasting volatility based on wavelet support vector machine}, author={Ling-Bing Tang and Ling-Xiao Tang and Huan-Ye Sheng}, journal={Expert Syst. Appl.}, year={2009}, volume={36}, pages={2901-2909} }

- Published 2009 in Expert Syst. Appl.
DOI:10.1016/j.eswa.2008.01.047

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a multidimensional wavelet kernel function and prove it meeting the mercer condition to address this… CONTINUE READING