Abstract
Recently, Heras et al. (2018. An application of two-stage quantile regression to insurance ratemaking. Scandinavian Actuarial Journal
9, 753-769) propose a two-step inference to forecast the Value-at-Risk of aggregated losses in insurance ratemaking by combining logistic regression and quantile regression without discussing the critical issue of uncertainty quantification. This paper proposes a random weighted bootstrap method to quantify the estimation uncertainty and an alternative two-step inference via weighted quantile regression.