Stochastic Loss Reserving: Dependence and Estimation
Insurers face complex risk dependencies in loss reserving. Additive background risk models (ABRMs) offer interpretable structures but can be restrictive. Estimation challenges arise in models without closed‑form likelihoods. Using a modified continuous generalized method of moments (CGMM), comparable to Maximum Likelihood Estimation (MLE), addresses these challenges in certain loss reserving models, including stable distributions.