Template-type: ReDIF-Paper 1.0
Author-Name: D. Kuang
Author-Workplace-Name: Lloyd's of London
Author-Email: di.kuang@lloyds.com
Author-Name: B. Nielsen
Author-Workplace-Name: Nuffield College, University of Oxford
Author-Email: bent.nielsen@nuffield.ox.ac.uk
Title: Generalized Log-Normal Chain-Ladder
Abstract: We propose an asymptotic theory for distribution forecasting from the log
normal chain-ladder model. The theory overcomes the difficulty of convoluting log
normal variables and takes estimation error into account. The results differ from that of
the over-dispersed Poisson model and from the chain-ladder based bootstrap. We embed
the log normal chain-ladder model in a class of infinitely divisible distributions called
the generalized log normal chain-ladder model. The asymptotic theory uses small σ
asymptotics where the dimension of the reserving triangle is kept fixed while the standard
deviation is assumed to decrease. The resulting asymptotic forecast distributions follow
t distributions. The theory is supported by simulations and an empirical application. 
Keywords: chain-ladder, infinitely divisibility, over-dispersed Poisson, bootstrap, lognormal.
Length: 30 pages
Creation-Date: 2018-03-14
Number: 2018-W02
File-URL: https://www.nuffield.ox.ac.uk/economics/Papers/2018/2018W02_KuangNielsen2018GLNCL.pdf
File-Format: application/pdf
Handle: RePEc:nuf:econwp:1802