VOLATILITY PREDICTION BY TREED GAUSSIAN PROCESS WITH LIMITING LINEAR MODEL

Phichhang Ou · Hengshan Wang

41 citations0/0 files storedInternational Journal of Modelling and Simulation

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Stock Market Forecasting Methods

Abstract

AbstractIn 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.

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