Severity Curve Fitting for Long­Tailed Lines: An Application of Stochastic Processes and Bayesian Models

Abstract

I present evidence for a model in which parameters fit to the severity distribution at each report age follow a smooth curve with random error. More formally, this is a stochastic process, and it allows us to estimate parameters of the ultimate severity distribution. I detail a Bayesian hierarchical model that takes a modestly sized dataset of triangulated individual claim data and returns posterior distributions for the parameters of the ultimate severity distribution, trend and loss to an excess layer. Currently available methods are also discussed. Full code and data are provided in the appendices.

Volume
11
Issue
1
Page
118-132
Year
2017
Keywords
Bayesian hierarchical models, stochastic processes, severity distributions, reinsurance pricing, predictive analytics
Categories
Financial and Statistical Methods
Statistical Models and Methods
Bayesian Methods
Financial and Statistical Methods
Loss Distributions
Severity
Business Areas
Reinsurance
Financial and Statistical Methods
Risk Pricing and Risk Evaluation Models
Publications
Variance
Authors
Greg McNulty