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...

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...

Value-at-Risk (VaR) forecasting via a computational Bayesian framework is considered. A range of parametric models are compared, including standard, threshold nonlinear and Markov switching GARCH specifications, plus standard and nonlinear stochastic volatility models, most considering four...

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately...

This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model and adopts a standardized Student-t and a standardized skewed Student-t distributions for the return equation. The Bayesian estimators show more...