We propose a nonlinear smooth transition conditional autoregressive range (CARR) model for capturing smooth volatility asymmetries in international financial stock markets, building on recent work on smooth transition conditional duration modelling. An adaptive Markov chain Monte Carlo scheme is...

A family of threshold nonlinear generalised autoregressive conditionally heteroscedastic models is considered, that allows smooth transitions between regimes, capturing size asymmetry via an exponential smooth transition function. A Bayesian approach is taken and an efficient adaptive sampling...

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing...

This paper proposes novel approaches to the modeling of attenuation bias effects in volatility forecasting. Our strategy relies on suitable generalizations of the Realized GARCH model by Hansen et al. (2012) where the impact of lagged realized measures on the current conditional variance is...

This paper proposes an ex post volatility estimator, called mixed interval realized variance (MIRV), that uses high-frequency data to provide measurements robust to the idiosyncratic noise of stock markets caused by market microstructures. The theoretical properties of the new volatility...

A two-sided Weibull is developed for modelling the conditional financial return distribution, for the purpose of forecasting tail risk measures. For comparison, a range of conditional return distributions are combined with four volatility specifications in order to forecast the tail risk in...