Assessing Risk Heterogeneity through Heavy-Tailed Frequency and Severity Mixtures

TL;DR

This paper develops heavy-tailed mixture models for risk analysis by combining Negative Binomial and Gamma distributions, enabling assessment of risk heterogeneity and robustness of inferences.

Contribution

It introduces canonical and flexible 4- and 5-parameter mixture models for frequency and severity, extending to arbitrary kernels for improved risk heterogeneity detection.

Findings

Derived conditions for heavy-tailed mixtures based on distribution duality.

Formulated flexible 4-parameter models for Geometric and Exponential kernels.

Extended models to arbitrary kernels, enabling robust risk heterogeneity assessment.

Abstract

The analysis of risk typically involves dividing a random damage-generation process into separate frequency (event-count) and severity (damage-magnitude) components. In the present article, we construct canonical families of mixture distributions for each of these components, based on a Negative Binomial kernel for frequencies and a Gamma kernel for severities. These mixtures are employed to assess the heterogeneity of risk factors underlying an empirical distribution through the shape of the implied mixing distribution. From the duality of the Negative Binomial and Gamma distributions, we first derive necessary and sufficient conditions for heavy-tailed (i.e., inverse power-law) canonical mixtures. We then formulate flexible 4-parameter families of mixing distributions for Geometric and Exponential kernels to generate heavy-tailed 4-parameter mixture models, and extend these mixtures to arbitrary Negative Binomial and Gamma kernels, respectively, yielding 5-parameter mixtures for detecting and measuring risk heterogeneity. To check the robustness of such heterogeneity inferences, we show how a fitted 5-parameter model may be re-expressed in terms of alternative Negative Binomial or Gamma kernels whose associated mixing distributions form a "calibrated" family.