Joint generalized quantile and conditional tail expectation regression for insurance risk analysis

Based on recent developments in joint regression models for quantile and expected shortfall, this paper seeks to develop analyse the risk right tail of distribution non-negative dependent random variables. We propose an algorithm estimate conditional expectation regressions, introducing generalized with link functions that are similar those linear models. To preserve natural ordering measures a set covariates, we add extra terms regression. A case using telematics data motor insurance illustrates practical implementation predictive their potential usefulness actuarial analysis.