ChainLadder (version 0.2.19)

BootChainLadder: Bootstrap-Chain-Ladder Model

Description

The BootChainLadder procedure provides a predictive distribution of reserves or IBNRs for a cumulative claims development triangle.

Usage

BootChainLadder(Triangle, R = 999, process.distr=c("gamma", "od.pois"), seed = NULL)

Value

BootChainLadder gives a list with the following elements back:

call

matched call

Triangle

input triangle

f

chain-ladder factors

simClaims

array of dimension c(m,n,R) with the simulated claims

IBNR.ByOrigin

array of dimension c(m,1,R) with the modeled IBNRs by origin period

IBNR.Triangles

array of dimension c(m,n,R) with the modeled IBNR development triangles

IBNR.Totals

vector of R samples of the total IBNRs

ChainLadder.Residuals

adjusted Pearson chain-ladder residuals

process.distr

assumed process distribution

R

the number of bootstrap replicates

Arguments

Triangle

cumulative claims triangle. Assume columns are the development period, use transpose otherwise. A (mxn)-matrix \(C_{ik}\) which is filled for \(k \le n+1-i; i=1,\ldots,m; m\ge n \). See qpaid for how to use (mxn)-development triangles with m<n, say higher development period frequency (e.g quarterly) than origin period frequency (e.g accident years).

R

the number of bootstrap replicates.

process.distr

character string indicating which process distribution to be assumed. One of "gamma" (default), or "od.pois" (over-dispersed Poisson), can be abbreviated

seed

optional seed for the random generator

Author

Markus Gesmann,

Details

The BootChainLadder function uses a two-stage bootstrapping/simulation approach. In the first stage an ordinary chain-ladder methods is applied to the cumulative claims triangle. From this we calculate the scaled Pearson residuals which we bootstrap R times to forecast future incremental claims payments via the standard chain-ladder method. In the second stage we simulate the process error with the bootstrap value as the mean and using the process distribution assumed. The set of reserves obtained in this way forms the predictive distribution, from which summary statistics such as mean, prediction error or quantiles can be derived.

References

England, PD and Verrall, RJ. Stochastic Claims Reserving in General Insurance (with discussion), British Actuarial Journal 8, III. 2002

Barnett and Zehnwirth. The need for diagnostic assessment of bootstrap predictive models, Insureware technical report. 2007

See Also

See also summary.BootChainLadder, plot.BootChainLadder displaying results and finally CDR.BootChainLadder for the one year claims development result.

Examples

# See also the example in section 8 of England & Verrall (2002) on page 55.

B <- BootChainLadder(RAA, R=999, process.distr="gamma")
B
plot(B)
# Compare to MackChainLadder
MackChainLadder(RAA)
quantile(B, c(0.75,0.95,0.99, 0.995))

# fit a distribution to the IBNR
library(MASS)
plot(ecdf(B$IBNR.Totals))
# fit a log-normal distribution 
fit <- fitdistr(B$IBNR.Totals[B$IBNR.Totals>0], "lognormal")
fit
curve(plnorm(x,fit$estimate["meanlog"], fit$estimate["sdlog"]), col="red", add=TRUE)

# See also the ABC example in  Barnett and Zehnwirth (2007) 
A <- BootChainLadder(ABC, R=999, process.distr="gamma")
A
plot(A, log=TRUE)

## One year claims development result
CDR(A)