Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of … volatility components. From a practical point of view, ML also becomes computationally unfeasible for large numbers of components … forecasts which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo …

forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility … which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo … linear compared to optimal forecasts is small. Extending the number of volatility components beyond what is feasible with MLE …

The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism … for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by … volatility models of asset returns. An intra-day data set for five major international stock market indices is used to evaluate …

This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market … returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily … realized volatility from the returns in the first step and use stochastic cusp catastrophe on data normalized by the estimated …

) Time varying volatility. (ii) Positive volume-volatility relation. (iii) Ambiguous volume-persistance relation. Finally …