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 …

by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and …

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular … practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also …

We investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular … practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also …

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as …. Liquidity is causal for future volatility but not vice versa. Furthermore, trade sizes are negatively driven by past trading …

The purpose of this paper is to examine the asymmetric relationship betweenprice and implied volatility and the … todemonstrate that the relationship between the volatility and market return as quantifiedby Ordinary Least Square (OLS) regression … is not uniform across the distributionof the volatility-price return pairs using quantile regressions. We examine …