chainladder | R Documentation |
Basic chain-ladder function to estimate age-to-age factors for a given cumulative run-off triangle. This function is used by Mack- and MunichChainLadder.
chainladder(Triangle, weights = 1, delta = 1)
Triangle |
cumulative claims triangle. A (mxn)-matrix |
weights |
weights. Default: 1, which sets the weights for all
triangle entries to 1. Otherwise specify weights as a matrix of the same
dimension as |
delta |
'weighting' parameters. Default: 1;
delta=1 gives the historical chain-ladder age-to-age factors,
delta=2 gives the straight average of the
observed individual development factors and delta=0 is the result of
an ordinary regression of Please note that |
The key idea is to see the chain-ladder algorithm as a special form of a weighted linear regression through the origin, applied to each development period.
Suppose y
is the vector of cumulative claims at development period
i+1
, and x
at development period i
, weights
are
weighting factors and F
the individual age-to-age factors F=y/x
. Then
we get the various age-to-age factors:
Basic (unweighted) linear regression through the origin:
lm(y~x + 0)
Basic weighted linear regression through the origin:
lm(y~x + 0, weights=weights)
Volume weighted chain-ladder age-to-age factors:
lm(y~x + 0, weights=1/x)
Simple average of age-to-age factors:
lm(y~x + 0, weights=1/x^2)
Barnett & Zehnwirth (2000) use delta = 0, 1, 2 to distinguish between the above
three different regression approaches: lm(y~x + 0, weights=weights/x^delta)
.
Thomas Mack uses the notation alpha = 2 - delta
to achieve the same result:
sum(weights*x^alpha*F)/sum(weights*x^alpha) # Mack (1999) notation
chainladder returns a list with the following elements:
Models |
linear regression models for each development period |
Triangle |
input triangle of cumulative claims |
weights |
weights used |
delta |
deltas used |
Markus Gesmann <markus.gesmann@gmail.com>
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
G. Barnett and B. Zehnwirth. Best Estimates for Reserves. Proceedings of the CAS. Volume LXXXVII. Number 167. November 2000.
See also
ata
,
predict.ChainLadder
MackChainLadder
,
## Concept of different chain-ladder age-to-age factors.
## Compare Mack's and Barnett & Zehnwirth's papers.
x <- RAA[1:9,1]
y <- RAA[1:9,2]
F <- y/x
## wtd. average chain-ladder age-to-age factors
alpha <- 1 ## Mack notation
delta <- 2 - alpha ## Barnett & Zehnwirth notation
sum(x^alpha*F)/sum(x^alpha)
lm(y~x + 0 ,weights=1/x^delta)
summary(chainladder(RAA, delta=delta)$Models[[1]])$coef
## straight average age-to-age factors
alpha <- 0
delta <- 2 - alpha
sum(x^alpha*F)/sum(x^alpha)
lm(y~x + 0, weights=1/x^(2-alpha))
summary(chainladder(RAA, delta=delta)$Models[[1]])$coef
## ordinary regression age-to-age factors
alpha=2
delta <- 2-alpha
sum(x^alpha*F)/sum(x^alpha)
lm(y~x + 0, weights=1/x^delta)
summary(chainladder(RAA, delta=delta)$Models[[1]])$coef
## Compare different models
CL0 <- chainladder(RAA)
## age-to-age factors
sapply(CL0$Models, function(x) summary(x)$coef["x","Estimate"])
## f.se
sapply(CL0$Models, function(x) summary(x)$coef["x","Std. Error"])
## sigma
sapply(CL0$Models, function(x) summary(x)$sigma)
predict(CL0)
CL1 <- chainladder(RAA, delta=1)
## age-to-age factors
sapply(CL1$Models, function(x) summary(x)$coef["x","Estimate"])
## f.se
sapply(CL1$Models, function(x) summary(x)$coef["x","Std. Error"])
## sigma
sapply(CL1$Models, function(x) summary(x)$sigma)
predict(CL1)
CL2 <- chainladder(RAA, delta=2)
## age-to-age factors
sapply(CL2$Models, function(x) summary(x)$coef["x","Estimate"])
## f.se
sapply(CL2$Models, function(x) summary(x)$coef["x","Std. Error"])
## sigma
sapply(CL2$Models, function(x) summary(x)$sigma)
predict(CL2)
## Set 'weights' parameter to use only the last 5 diagonals,
## i.e. the last 5 calendar years
calPeriods <- (row(RAA) + col(RAA) - 1)
(weights <- ifelse(calPeriods <= 5, 0, ifelse(calPeriods > 10, NA, 1)))
CL3 <- chainladder(RAA, weights=weights)
summary(CL3$Models[[1]])$coef
predict(CL3)
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