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 …

volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …

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 …, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …

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 …