conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain … variance, in the order of integration, in the short memory characteristics and in the volatility of volatility. …

We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete … series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades … intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling …

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 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 …