. Announcements of both institutes are also clearly and immediately reflected in the volatility, which remains at a significantly … higher level for approximately two minutes slightly elevated for approximately 15 minutes. Combining returns and volatility … in a GARCH(1,1)-model, the paper reveals that significant increases in volatility only show up in the presence of …

direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply … London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However …

differences of opinion is left, and hence volatility is decreased. …

direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply … London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However …

differences of opinion is left, and hence volatility is decreased. …

We propose a new approach to measuring the effect of unobservable private information or beliefs on volatility. Using … high-frequency intraday data, we estimate the volatility effect of a well identified shock on the volatility of the stock … that, as the publicly available information becomes stale, volatility effects and its persistence should increase, as the …

reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and … significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow …

reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and … significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow …

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 …