. The methodology is hybrid because it combines a formaltesting procedure with volatility curve pattern recognition based …

methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean …(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable … Stochastic Volatility (SV)model. However, efficient Monte Carlo simulationmethods for SV models have been developed to overcome …

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 …

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 …

methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …

that is embedded in the time-varying parameter path. We illustrate our findings in a volatility analysis for monthly …