Performance evaluation of the Bayesian and classical value at risk models with circuit breakers set up

Circuit breakers, like price limits and trading suspensions, are used to reduce price volatility in security markets. When returns hit price limits or missed, observed returns deviate from equilibrium returns. This creates a challenge for predicting stock returns and modelling value at risk (VaR). In Tehran Stock Exchange (TSE), the circuit breakers are applied to control for the excess price volatilities. This paper intend to address which models and what methodology should be applied by risk analysts to calculate the VaR when the returns are unobservable. To this end, we extend Wei's (2002) model, in the framework of Bayesian Censored and Missing-GARCH approach, to estimate VaR for a share index in TSE. Using daily data over June 2006 to June 2016, we show that the Censored and Missing- GARCH model with student-t distribution outperforms the other VaR estimation metods. Kullback-Leibler (KLIC), Kupic (1995) test and Lopez score (1998) outcomes show that estimated VaR by Censored and missing- GARCH model with student-t distribution is of the most accuracy among the other GARCH family estimated models.