2024/10/19 by Andrew Fleck, Fleck, Andrew, Edward Furman +3
Decision Sciences · Engineering · #91B30 #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME) #Probability and Risk Models #Reliability and Maintenance Optimization #Risk Management (q-fin.RM)
paper · pdf · doi:10.48550/arxiv.2410.14985
openalex publication_date 2024/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Nowadays insurers have to account for potentially complex dependence between risks. In the field of loss reserving, there are many parametric and non-parametric models attempting to capture dependence between business lines. One common approach has been to use additive background risk models (ABRMs) which provide rich and interpretable dependence structures via a common shock model. Unfortunately, ABRMs are often restrictive. Models that capture necessary features may have impractical to estimate parameters. For example models without a closed-form likelihood function for lack of a probability density function (e.g. some Tweedie, Stable Distributions, etc). We apply a modification of the continuous generalised method of moments (CGMM) of [Carrasco and Florens, 2000] which delivers comparable estimators to the MLE to loss reserving. We examine models such as the one proposed by [Avanzi et al., 2016] and a related but novel one derived from the stable family of distributions. Our CGMM method of estimation provides conventional non-Bayesian estimates in the case where MLEs are impractical.