(This article was first published on

This is the third post about Christofides' paper on **mages' blog**, and kindly contributed to R-bloggers)*[1]. The first post covered the fundamentals of Christofides' reserving model in sections A - F, the second focused on a more realistic example and model reduction of sections G - K. Today's post will wrap up the paper with sections L - M and discuss data normalisation and claims inflation.*

**Regression models based on log-incremental payments**I will use the same triangle of incremental claims data as introduced in my previous post. The final model had three parameters for origin periods and two parameters for development periods. It is possible to reduce the model further as Christofides illustrates in section L onwards by using an inflation index to bring all claims payments to current value and a claims volume adjustment or weight for each origin period to normalise the triangle.

In his example Christofides uses claims volume adjustments for the origin years and an earning or inflation index for the different payment calendar years. The claims volume adjustments aims to normalise the triangle for similar exposures across origin periods, while the earnings index, which measures largely wages and other forms of compensations, is used as a first proxy for claims inflation. Note that the earnings index shows significant year on year changes from 5% to 9%. Barnett and Zehnwirth [2] would probably recommend to add further parameters for the calendar year effects to the model.

`# Page D5.36`

ClaimsVolume <- data.frame(origin=0:6,

volume.index=c(1.43, 1.45, 1.52, 1.35, 1.29, 1.47, 1.91))

# Page D5.36

EarningIndex <- data.frame(cal=0:6,

earning.index=c(1.55, 1.41, 1.3, 1.23, 1.13, 1.05, 1))

# Year on year changes

round((1-EarningIndex$earning.index[-1]/EarningIndex$earning.index[-7]),2)

# [1] 0.09 0.08 0.05 0.08 0.07 0.05

dat <- merge(merge(dat, ClaimsVolume), EarningIndex)

# Normalise data for volume and earnings

dat$logvalue.ind.inf <- with(dat, log(value/volume.index*earning.index))

`with(dat, interaction.plot(dev, origin, logvalue.ind.inf))`

points(1+dat$dev, dat$logvalue.ind.inf, pch=16, cex=0.8)

Indeed, the interaction plot shows the various origin years now to be much more closely grouped. Only the single point of the last origin period stands out now. Christofides tests several models with different numbers of origin levels, but I am happy with the minimal model using only one parameter for the origin period, namely the intercept:Read more »To

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