DCL-package {DCL}

Claims Reserving under the Double Chain Ladder Model

Description

This package provides functions for statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012). Using specific functions, the user will be able generate plots to visualize and gain intuition about the data (run-off triangles), break down classical chain ladder under the DCL model, visualize the underlying delay function and the inflation, introduce expert knowledge about the severity inflation, the zero-claims etc. Besides a validation exercise can be performed through a back-test on the data.

Details

Package:	DCL
Type:	Package
Version:	0.1.0
Date:	2013-10-24
License:	GPL-2

Author(s)

M.D. Martinez-Miranda, J.P. Nielsen and R. Verrall

Maintainer: Maria Dolores Martinez-Miranda <[e-mail]>

References

Martinez-Miranda M.D., Nielsen B, Nielsen J.P and Verrall, R. (2011) Cash flow simulation for a model of outstanding liabilities based on claim amounts and claim numbers. Astin Bulletin, 41/1, 107-129.

Martinez-Miranda, M.D., Nielsen, J.P. and Verrall, R. (2012) Double Chain Ladder. Astin Bulletin, 42/1, 59-76.

Martinez-Miranda, M.D., Nielsen, J.P. and Verrall, R. (2013) Double Chain Ladder and Bornhuetter-Ferguson. North American Actuarial Journal, 17(2), 101-113.

Martinez-Miranda, M.D., Nielsen, J.P., Verrall, R. and Wuthrich, M.V. (2013) Double Chain Ladder, Claims Development Inflation and Zero Claims. Scandinavian Actuarial Journal. In press.

See more at http://www.cassknowledge.com/research/article/double-chain-ladder-cass-knowledge

Examples

data(NtriangleDCL)
data(XtriangleDCL)

# Classical chain ladder parameters
my.clm.par<-clm(XtriangleDCL)
Plot.clm.par(my.clm.par)

# Estimation of the DCL parameters (break-down of the chain ladder parameters)
my.dcl.par<-dcl.estimation(XtriangleDCL,NtriangleDCL)
Plot.dcl.par(my.dcl.par)

# DCL Predictions by diagonals (future calendar years)
# Splitting the chain ladder reserve into RBNR and IBNR claims (ignoring the tail)
preds.dcl.diag<-dcl.predict(my.dcl.par,Model=0,Tail=FALSE,num.dec=0)

# Full cashflow considering the tail (only the variance process)
# Below only B=200 simulations for faster calculations in the example
boot1<-dcl.boot(dcl.par=my.dcl.par,Ntriangle=NtriangleDCL,boot.type=1,B=200)
Plot.cashflow(boot1)