forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

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 …

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 …

, an estimation of volatility in the DAX is provided. Such estimation is then plugged into a quantile regression model …This paper addresses stock market volatility in Germany between 1991 and 2018. Through a GARCH model with leverage term … where potential economic determinants are analyzed. The results suggest that stock market volatility in Germany reached its …

It has been established in the literature that volatility of stock returns exhibits complex properties of not only … volatility clustering, but also long memory, regime change, and substantial outliers during turbulent and calm periods. Hence …, this paper seeks to analyze volatility spillover, co-movements, independence and contagion in the Chinese, Japanese …