The performance of time-varying volatility and regime switching models in estimating Value-at-Risk
Markov Regime-Switching GARCH (MRS-GARCH) models have been gaining popularity due to their ability to account for shifts volatility regimes that tend to characterize returns series. Previous empirical studies have shown that this capacity to capture the volatility dynamics leads to a superior forecasting power of the MRS models. We investigate the performance of these models in quantifying and man
