Year: 2019
Type: dissertation
Abstract: Bayesian Inference is used to develop a credibility estimator and a \n method to compute insurance premium risk loadings. Algorithms to \n apply both methods to Generalized Linear Models (GLMs) are provided. \n We call our credibility estimator the entropic premium. It is \n a Bayesian point estimator that uses the relative entropy as the loss \n function. The risk measures Value-at-Risk (VaR) and \n Tail-Value-at-Risk (TVaR) are used to determine premium risk \n loadings. Our method considers the number of insureds and their \n durations as random variables. A distribution to model the duration \n of risks is introduced. We call it unifed, it has support on \n the interval (0,1), it is an exponential dispersion family and it \n can be used as the response distribution of a GLM.
Authors: Quijano Xacur, Oscar Alberto, Oaqx
Language: English
Topic: Probability and Risk Models
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2986459033