Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility … highfrequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing … observable or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA …

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been … employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which … can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics …

, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments … implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors …

) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast …

-making approach, communication strategy and operational framework of a Central bank. Through a GARCH specification we show that the … USA and Euro area displayed a limited but significant spillover of volatility from money market to longer-term rates. We … evidence that the transmission of overnight volatility along the yield curve vanished soon after specific policy changes of the …