In this paper, we introduce a Markov-switching generalized autoregressive conditional heteroscedasticity (GARCH) model in the short-time Fourier transform (STFT) domain. A GARCH model is utilized with Markov switching regimes, where the parameters are assumed to be frequency variant. The model parameters are evaluated in each frequency subband and a special state (regime) is de ned for the case...

Many researchers use GARCH models to generate volatility forecasts. Using data on three major U.S. dollar exchange rates we show that such forecasts are too high in volatile periods. We argue that this is due to the high persistence of shocks in GARCH forecasts. To obtain more flexibility regarding volatility persistence, this paper generalizes the GARCH model by distinguishing two regimes with...

This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence” found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the ...

In this paper, we examine the relationship between volatilities of energy index, crude oil, gas prices, and financial assets (Gold, Bitcoin, G7 stock indexes), especially during coronavirus crisis. The study tests presence regime changes in GARCH volatility dynamics indexes, Gold, (energy gas) by using Markov–Switching model. It estimates dynamic correlation spillover assets, multivariate MSGAR...

Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on all the past history of state. This paper shows that this problem can be handled easily in Bayesian in...