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R package for reserving in non life insurance
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DESCRIPTION
NAMESPACE
README.md

README.md

Reserving for non life insurance

This package is a light compilation of useful code for non life reserving in insurance.

Be careful if you are using it. It was mainly developed for self-teaching and is not as reliable as the ChainLadder package that already exists. Use it to your own risks.

Installing the package

You can install the package by using the r install_github function from the devtools package.

library(devtools)
devtools::install_github("arnaudbu/ReservingLad", dependencies = TRUE)

The warnings are normal. They just come from some conflicts between the libraries.

Type of data

The triangles must be entered under the format of a numeric matrix, with NAs used for unavailable values. A triangle is provided as an example:

data(triangleExampleEngland)
triangleExampleEngland
        1       2       3       4       5       6       7       8       9      10
1  357848 1124788 1735330 2218270 2745596 3319994 3466336 3606286 3833515 3901463
2  352118 1236139 2170033 3353322 3799067 4120063 4647867 4914039 5339085      NA
3  290507 1292306 2218525 3235179 3985995 4132918 4628910 4909315      NA      NA
4  310608 1418858 2195047 3757447 4029929 4381982 4588268      NA      NA      NA
5  443160 1136350 2128333 2897821 3402672 3873311      NA      NA      NA      NA
6  396132 1333217 2180715 2985752 3691712      NA      NA      NA      NA      NA
7  440832 1288463 2419861 3483130      NA      NA      NA      NA      NA      NA
8  359480 1421128 2864498      NA      NA      NA      NA      NA      NA      NA
9  376686 1363294      NA      NA      NA      NA      NA      NA      NA      NA
10 344014      NA      NA      NA      NA      NA      NA      NA      NA      NA

Triangle Manipulation

Create Triangle

The function Payments2Triangle can convert a list of payment to a cumulated triangle for reserving purpose. Check the help for more information.

Cumulate

decTriangle <- Decumulate(triangleExampleEngland)
cumTriangle <- Cumulate(triangleExampleEngland)
cumTriangle

Decumulate

decTriangle <- Decumulate(triangleExampleEngland)
decTriangle
        1       2       3       4      5      6      7      8      9    10
1  357848  766940  610542  482940 527326 574398 146342 139950 227229 67948
2  352118  884021  933894 1183289 445745 320996 527804 266172 425046    NA
3  290507 1001799  926219 1016654 750816 146923 495992 280405     NA    NA
4  310608 1108250  776189 1562400 272482 352053 206286     NA     NA    NA
5  443160  693190  991983  769488 504851 470639     NA     NA     NA    NA
6  396132  937085  847498  805037 705960     NA     NA     NA     NA    NA
7  440832  847631 1131398 1063269     NA     NA     NA     NA     NA    NA
8  359480 1061648 1443370      NA     NA     NA     NA     NA     NA    NA
9  376686  986608      NA      NA     NA     NA     NA     NA     NA    NA
10 344014      NA      NA      NA     NA     NA     NA     NA     NA    NA

Hypothesis testing

First Mack hypothesis

Plot to show the possibility to use the development factors

MackFirstHyp(triangleExampleEngland)

Second Mack hypothesis

Test the independance of the residuals

MackSecondHyp(triangleExampleEngland)

Chain Ladder hypothesis

Test the independance of residuals regarding the calendar year.

ChainLadderHyp(triangleExampleEngland)

Chain Ladder

outputCL <- ChainLadder(triangleExampleEngland)
outputCL

Bornhuetter Fergusson

ultimateClaims <- c(3901463,5433719,5378826,5297906,4858200,5111171,5660771,6784799,5642266,4969825)
outputBF <- BornFerg(triangleExampleEngland, ultimateClaims)
outputBF

Mack 93 variance estimation

error <- Mack93Variance(triangleExampleEngland)
error

Stochastic evaluations

Poisson bootstraping

bcl <- BootstrapChainLadder(triangleExampleEngland, 1000)
mean(bcl$ibnr)
bcl$predictionError

Mack bootstraping

bm <- BootstrapMack(triangleExampleEngland, 1000)
mean(bm$ibnr)
bm$predictionError
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