characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …

Das in Finanzmarktdaten zu beobachtende volatility-clustering impliziert, daß große Renditeschocks bei der Preisbildung …The volatility clustering observed in financial market data implies that large net yield shocks increase the … probability of a higher future volatility during the price formation. Starting from the ARCH models which were suggested by Engle …

in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid … that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH …-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory …

volatility at the turn of the millennium when Japanese foreign exchange intervention started to remain unsterilized. …

describe the most typical features of capital markets like volatility clustering, excess kurtosis and fat tails. As empirical … evidence shows asymmetry is also a prominent feature of stock market returns volatility. The reaction of risk if stock returns …

making up the Dow Jones Industrial Index, I calculate intraday upside and downside volatility measures, following Becker et … document that for all the stocks in the sample, mean daily returns following the days when a stock's upside volatility measure … was higher or equal to its downside volatility measure are higher than following the days when the opposite relationship …

(2000) is applied but generalised to account for weekly periodicities and time-varying volatility. Eventually we find a …