ChainLadder (version 0.1.1-1)

ChainLadder-package: Mack- and Munich-chain-ladder models for claims reserving

Description

The ChainLadder-package grew out of presentations the author gave at the Stochastic Reserving Seminar at the Institute of Actuaries in November 2007. This package implements the Mack- and Munich-chain-ladder model using weighted linear regression, see lm. An example spreadsheet of how you might want to use these functions in Excel is available in the installation directory of this package. Usually along the line C:/Programmes/R/R-Version/library/ChainLadder/Excel. You need the RExcel-Addin, from http://sunsite.univie.ac.at/rcom/, in order to use the spreadsheet.

Details

ll{ Package: ChainLadder Type: Package Version: 0.1.2 Date: 2007-12-05 License: GPL version 2 or later }

References

Thomas Mack. Distribution-free calculation of the standard error of chain ladder reserve estimates. Astin Bulletin. Vol. 23. No 2. 1993. pp.213:225

Thomas Mack. The standard error of chain ladder reserve estimates: Recursive calculation and inclusion of a tail factor. Astin Bulletin. Vol. 29. No 2. 1999. pp.361:366

Gerhard Quarg and Thomas Mack. Munich Chain Ladder. Blatter DGVFM 26, Munich, 2004.

Examples

# Example data sets

# Run off triangle of accumulated claims data. A data frame with 10 underwriting years and 10 
# development years.
# Source:Historical Loss Development, Reinsurance Association of America (RAA), 1991, p.96
# See Also: Which Stochastic Model is Underlying the Chain Ladder Method?, Thomas Mack, 1994,
# Insurance Mathematics and Economics, 15, 2/3, 133-138
#
# P.D.England and R.J.Verrall, Stochastic Claims Reserving in General Insurance, 
# British Actuarial Journal, Vol. 8, pp443-544, 2002
RAA <- t(matrix(c(
  5012,  8269, 10907, 11805, 13539, 16181, 18009, 18608, 18662, 18834,
   106,  4285,  5396, 10666, 13782, 15599, 15496, 16169, 16704,    NA,
  3410,  8992, 13873, 16141, 18735, 22214, 22863, 23466,    NA,    NA,
  5655, 11555, 15766, 21266, 23425, 26083, 27067,    NA,    NA,    NA,
  1092,  9565, 15836, 22169, 25955, 26180,    NA,    NA,    NA,    NA,
  1513,  6445, 11702, 12935, 15852,    NA,    NA,    NA,    NA,    NA,
   557,  4020, 10946, 12314,    NA,    NA,    NA,    NA,    NA,    NA,
  1351,  6947, 13112,    NA,    NA,    NA,    NA,    NA,    NA,    NA,
  3133,  5395,    NA,    NA,    NA,    NA,    NA,    NA,    NA,    NA,
  2063,    NA,    NA,    NA,    NA,    NA,    NA,    NA,    NA,    NA
  ), ncol=10))
  


 MCL=MackChainLadder(RAA)
 MCL
 plot(MCL)


 # Munich Chain Ladder
Paid <- t(matrix(c(
  576, 1804, 1970, 2024, 2074, 2102, 2131,
  866, 1948, 2162, 2232, 2284, 2348,   NA,
 1412, 3758, 4252, 4416, 4494,   NA,   NA,
 2286, 5292, 5724, 5850,   NA,   NA,   NA,
 1868, 3778, 4648,   NA,   NA,   NA,   NA,
 1442, 4010,   NA,   NA,   NA,   NA,   NA,
 2044,   NA,   NA,   NA,   NA,   NA,   NA
 ), ncol=7))
 	
Incurred <- t(matrix(c( 
  978, 2104, 2134, 2144, 2174, 2182, 2174,
 1844, 2552, 2466, 2480, 2508, 2454,   NA,
 2904, 4354, 4698, 4600, 4644,   NA,   NA,
 3502, 5958, 6070, 6142,   NA,   NA,   NA,
 2812, 4882, 4852,   NA,   NA,   NA,   NA,
 2642, 4406,   NA,   NA,   NA,   NA,   NA,
 5022,   NA,   NA,   NA,   NA,   NA,   NA
 ), ncol=7))

MCL = MunichChainLadder(Paid, Incurred)
MCL
plot(MCL)