Stochastic loss reserving: A new perspective from a Dirichlet model
Karthik Sriram and
Journal of Risk & Insurance, 2021, vol. 88, issue 1, 195-230
Forecasting the outstanding claim liabilities to set adequate reserves is critical for a nonlife insurer's solvency. Chain–Ladder and Bornhuetter–Ferguson are two prominent actuarial approaches used for this task. The selection between the two approaches is often ad hoc due to different underlying assumptions. We introduce a Dirichlet model that provides a common statistical framework for the two approaches, with some appealing properties. Depending on the type of information available, the model inference naturally leads to either Chain–Ladder or Bornhuetter–Ferguson prediction. Using claims data on Worker's compensation insurance from several U.S. insurers, we discuss both frequentist and Bayesian inference.
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:jrinsu:v:88:y:2021:i:1:p:195-230
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Risk & Insurance is currently edited by Joan T. Schmit
More articles in Journal of Risk & Insurance from The American Risk and Insurance Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().