Bayesian Reserving Models Inspired by Chain Ladder Methods and Implemented Using WinBUGS

David P.M. Scollnik
Department of Mathematics and Statistics
University of Calgary

ABSTRACT

This paper examines some new Bayesian models for loss reserving inspired by a consideration of some of the methods and techniques appearing in the traditional chain ladder literature.� This includes a possibly order restricted hierarchical Bayesian model for the year over year development factors.� A new Bornhuetter-Ferguson styled Bayesian method of estimate revision is also discussed. These Bayesian models are implemented using Markov chain Monte Carlo (MCMC) methods in WinBUGS (a software program for MCMC simulations).� Illustrative WinBUGS code is included.