A1322
Title: Forecasting and change point test for nonlinear heteroscedastic time series based on support vector regression
Authors: Meihui Guo - National Sun Yat-sen University (Taiwan) [presenting]
Abstract: SVR-ARMA-GARCH models provide flexible model fitting and good predictive powers for nonlinear heteroscedastic time series datasets. The change point detection problem in the SVR-ARMA-GARCH model using the residual-based CUSUM test is explored. For this task, an alternating recursive estimation (ARE) method is proposed to improve the estimation accuracy of residuals. Moreover, using a new testing method with a time-varying control limit significantly improves the detection power of the CUSUM test is suggested. The numerical analysis exhibits the merits of the proposed methods in SVR-ARMA-GARCH models. A real data example is also conducted using BDI data for illustration, which also confirms the validity of the methods.
