We introduce SV models with Markov regime changing state equation (SVMRS) to investigate the important properties of volatility, high persistence and smoothness. With the quasi-ML approach proposed in our study, we showed that volatility is far less persistent and smooth than the GARCH or SV...

It is shown that the ML estimates of the popular GARCH(1,1) model are significantly negatively biased in small samples and that in many cases converged estimates are not possible with Bollerslev's non-negativity conditions. Results also indicate that a high level of persistence in GARCH(1,1)...

We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than...

We show that persistence of conditional volatility in large samples could be exaggerated by the existence of structural breaks in the ARCH and GARCH parameters. Our results suggest that extreme persistence frequently observed in index volatility does not necessarily indicate the same level of...

We show that persistence of conditional volatility in large samples could be exaggerated by the existence of structural breaks in the ARCH and GARCH parameters. Our results suggest that extreme persistence frequently observed in index volatility does not necessarily indicate the same level of...