Stochastic Reserving

The source provides an extensive overview of stochastic claims reserving models, which are necessary to move beyond single-point estimates and quantify the uncertainty surrounding insurance reserves. It details the various sources of reserving uncertainty, which include random factors like claim severity and legal changes, as well as model, parameter (estimation), and process errors. The document outlines the uses and benefits of these stochastic methods, such as assessing reserve adequacy and informing capital allocation, and categorizes the models into three types: analytic methods (like Mack and ODP), simulation-based methods (including bootstrapping), and Bayesian methods. Crucially, it discusses the implementation details, advantages, and drawbacks of specific models, including how to test their appropriateness and address issues like underestimation of variability and data sparsity. Finally, it addresses the important topic of aggregating results across multiple lines of business by modeling correlations, often using copulas.