Ling-Bing Tang

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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(More)
Volatility forecasting is vital important in finance to reduce risk and take better decisions. This paper proposes a spline wavelet support vector machine (SWSVM) to forecast the volatility of financial time series based on generalized autoregressive conditional heteroscedasticity model. An admissible spline wavelet kernel is constructed by incorporating(More)
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine (MWSVM) for forecasting stock returns. The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine (SVM). Since manifold wavelet function can yield features that describe of the stock time(More)
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