Phichhang Ou and Hengshan Wang
Treed Gaussian processes, limiting linear model, GARCH, volatility forecasting
In this paper, we present new hybrid models of ﬁnancial volatility
using treed Gaussian processes with jumps to the limiting linear
model (TGPLLM) based on GARCH, EGARCH, and GJR models.
The TGPLLM is modelled as three diﬀerent volatility forms denoted
as TGPLLM-GARCH, TGPLLM-EGARCH, and TGPLLM-GJR.
Each of these models is trained by Matern family of correlation
function as the correlation function is the heart of the Gaussian
process. Hang Seng Index of Hong Kong stock market is analysed to
check the predictive accuracy of the proposed models. The empirical
results show that the hybrid models contribute improved predictive
capability rather than the stationary GARCH, EGARCH, and GJR
models for all cases. It is found that the hybrid Gaussian processes
can signiﬁcantly capture leverage eﬀect of news.