We compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and … for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed … model to the historical ISE-100 return data indicates that the return volatility reacts to bad news 24% more than they react …

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and … indices and foreign exchange rates. -- Stochastic volatility ; Markov chain Monte Carlo ; Metropolis-Hastings algorithm Jump …

We put forward two jump-robust estimators of integrated volatility, namely realized information variation (RIV) and …, comparing with alternative methods. The simulations support our theoretical results on volatility estimation and demonstrate …

and the short-run or transitory components of the volatility of daily stock market returns using bivariate mixture models …. For this purpose, the Standard bivariate mixture model of Tauchen and Pitts (1983) in which volatility and volume are … each endowed with their own dynamic behavior are allowed to direct volatility and volume. Since the latent information …

. Proposed extensions include a volatility regime switching mechanism (using dummy variables and the Markov approach) and the … fifth risk factor based on realized volatility of index returns. Moreover, instead of using data for stocks of a particular …

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

leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and … implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The … leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect …