An Observation-Driven State-Space Model for Claims Size Modeling

Abstract

State-space models are popular models in econometrics. Recently, these models have gained some popularity in the actuarial literature. The best known state-space models are of Kalman-filter type. These models are so-called parameter-driven because the observations do not impact the state-space dynamics. A second less well-known class of state-space models are so-called observation-driven state-space models where the state-space dynamics is also impacted by the actual observations. A typical example is the Poisson-Gamma observation-driven state-space model for counts data. This Poisson-Gamma model is fully analytically tractable. The goal of this paper is to develop a Gamma- Gamma observation-driven state-space model for claim size modeling. We provide fully tractable versions of Gamma-Gamma observation-driven state-space models, and these versions extend the work of Smith and Miller (1986) by allowing for a fully flexible variance behavior. Additionally, we demonstrate that the proposed model aligns with evolutionary credibility, a methodology in insurance that dynamically adjusts premium rates over time using evolving data.