A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

Chao Wang; Richard Gerlach

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arXiv · 2106.00288

A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

Chao Wang·Richard Gerlach

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Abstract

This paper proposes an innovative threshold measurement equation to be employed in a Realized-GARCH framework. The proposed framework incorporates a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed realized measure and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted and employed for model estimation, with its validity assessed via a simulation study. The validity of incorporating the proposed measurement equation in Realized-GARCH type models is evaluated via an empirical study, forecasting the 1% and 2.5% Value-at-Risk and Expected Shortfall on six market indices with two different out-of-sample sizes. The proposed framework is shown to be capable of producing competitive tail risk forecasting results in comparison to the GARCH and Realized-GARCH type models.

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BibTeX RIS

Chao Wang, Richard Gerlach. 2022-10-30. A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting. https://arxiv.org/abs/2106.00288

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