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 paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized …. The volatility and co-volatility spillovers are examined via the news impact curves and the impulse response functions … from returns to volatility and co-volatility. …

accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an … volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to … that time-varying volatility is only present in the a selection of idiosyncratic components while the coefficients driving …

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

Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH …) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative … shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH …