Apr 14, 2012
Forecasting Volatility Based on Wavelet Support Vector Machine
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 problem. The applicability and validity of wavelet support vector machine (WSVM) for volatility forecasting are confirmed through computer simulations and experiments on realworld stock data.
Authors
Ling-Bing Tang
Ling-Xiao Tang
Huan-Ye Sheng
