Joint generalized quantile and conditional tail expectation regression for insurance risk analysis
基于分位数与期望损失联合回归的最新进展，提出估计条件尾部期望回归的算法，引入类似广义线性模型的链接函数，并通过车险远程信息数据展示其在精算分析中的实用性。
Based on recent developments in joint regression models for quantile and expected shortfall, this paper seeks to develop models to analyse the risk in the right tail of the distribution of non-negative dependent random variables. We propose an algorithm to estimate conditional tail expectation regressions, introducing generalized risk regression models with link functions that are similar to those in generalized linear models. To preserve the natural ordering of risk measures conditional on a set of covariates, we add extra non-negative terms to the quantile regression. A case using telematics data in motor insurance illustrates the practical implementation of predictive risk models and their potential usefulness in actuarial analysis.