2024/01/15 by Marie Michaelides, Michaelides, Marie, Hélène Cossette +3
Decision Sciences · Economics, Econometrics and Finance · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Insurance and Financial Risk Management #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #Probability and Risk Models
paper · pdf · doi:10.48550/arxiv.2401.07724
openalex publication_date 2024/01/15 · openalex created_date 2024/01/18 · openalex updated_date 2026/07/28
With insurers benefiting from ever-larger amounts of data of increasing complexity, we explore a data-driven method to model dependence within multilevel claims in this paper. More specifically, we start from a non-parametric estimator for Archimedean copula generators introduced by Genest and Rivest (1993), and we extend it to diverse flexible censoring scenarios using techniques derived from survival analysis. We implement a graphical selection procedure for copulas that we validate using goodness-of-fit methods applied to complete, single-censored, and double-censored bivariate data. We illustrate the performance of our model with multiple simulation studies. We then apply our methodology to a recent Canadian automobile insurance dataset where we seek to model the dependence between the activation delays of correlated coverages. We show that our model performs quite well in selecting the best-fitted copula for the data at hand, especially when the dataset is large, and that the results can then be used as part of a larger claims reserving methodology.