  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.
