We study conditional correlations between pairs of risks in normal variance mixture models, which are widely used in risk management and finance. In particular, we examine up- and down-correlations defined as the conditional correlation between the sum of risks and an individual component,...

The Lee-Carter model, the dominant mortality projection modeling in the literature, was criticized for its homoscedastic error assumption. This was corrected in extensions to the model based on the assumption that the number of deaths follows Poisson or negative binomial distributions. We...

In automobile insurance, it is common to adopt a Poisson regression model to predict the number of claims as part of the actuarial pricing process. The Poisson assumption can rarely be justified, often due to overdispersion, and alternative modeling is often considered, typically zero-inflated...

We analyse a large telematics data with 65 driving variables (DVs) and insurance claim counts for 14,157 drivers. Our aim is to predict future claims according to driving behaviour measured by the DVs, to identify important DVs that differentiate driving behaviours, classify drivers according to...

This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of...