FORECASTING VOLATILITY SWITCHING ARCH BY TREED GAUSSIAN PROCESS WITH JUMPS TO THE LIMITING LINEAR MODEL

摘要

In this paper, we propose a new hybrid model of asymmetric volatility by using treed Gaussian process with jumps to the limiting linear model (TGPLLM) of Gramacy and Lee combined with the volatility switching ARCH (VS-ARCH) developed by Fornari and Mele to model and predict stock market volatility. Nonparametric sensitivity analysis based on the TGPLLM is applied to check the relevance level of five input variables in the model. Meanwhile, support vector machine is also employed to obtain another new hybrid model for making a comparison with the former. Empirical analysis of NASDAQ index reveals that the five input variables are all significant; the hybrid model based on TGPLLM yields better predictive performance than the ones based on SVM, the parametric models of VS-ARCH, ARMA-GARCH and ARMA-GJR models.