Improved Estimation of Parametric Tail Factor Models

Loss development tail factors are difficult to estimate due to training data that is typically quite sparse and volatile. We propose a set of techniques to better estimate tail factors from loss triangles. Each of these techniques is well grounded in statistical theory and relatively easy to implement. These techniques are applicable to a broad array of parametric tail factor models in the actuarial literature. We demonstrate that the use of these techniques results in more accurate tail factor estimates than the estimators typically used in practice.