Robust Predictive Model Using Copulas

This article presents a robust predictive model using parametric copula-based regression. We show that copula selection test procedures and predictive conditional distributions can be used to assess model adequacy and predictive validity. We offer simulation experiments to demonstrate the ability of our diagnostic procedure to correctly identify the true data generating process. Finally, we apply our methodology on a well-known insurance claims dataset to produce the distribution profile of allocated loss adjustment expense for given pre-specified indemnity payments information. The availability of this entire expense distribution will provide greater insight to the decision-makers before allocating resources for a given insurance claim.