In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive … effects on conditional volatility of positive and negative effects of equal magnitude, and possibly also leverage, which is … the negative correlation between returns shocks and subsequent shocks to volatility (see Black 1979). McAleer (2014 …

The three most popular univariate conditional volatility models are the generalized autoregressive conditional … models are important in estimating and forecasting volatility, as well as capturing asymmetry, which is the different effects … on conditional volatility of positive and negative effects of equal magnitude, and leverage, which is the negative …

improved volatility measurements but has also inspired research into their potential value as an information source for … volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return … series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … the stochastic volatility model with asymmetry and long memory, using high frequency data for the underlying asset …