ChainLadder (version 0.1.7)

glmReserve: GLM-based Reserving Model

Description

This function implements loss reserving models within the generalized linear model framework. It takes accident year and development lag as mean predictors in estimating the ultimate loss reserves, and provides both analytical and bootstrapping methods to compute the associated prediction errors. The bootstrapping approach also generates the full predictive distribution for loss reserves.

Usage

glmReserve(triangle, var.power = 1, link.power = 0, cum = TRUE, 
      mse.method = c("formula", "bootstrap"),  nsim = 1000, ...)

Arguments

triangle

An object of class triangle.

var.power

The index (p) of the power variance function $V(\mu)=\mu^p$. Default to p = 1, which is the over-dispersed Poisson model. If NULL, it will be assumed to be in (1, 2) and estimated using the cplm package.

link.power

The index of power link function. The default link.power = 0 produces a log link. See tweedie.

cum

A logical value indicating whether the input triangle is on the cumulative or the incremental scale. If TRUE, then triangle is assumed to be on the cumulative scale, and it will be converted to incremental losses internally

mse.method

A character indicating whether the prediction error should be computed analytically (mse.method = "formula") or via bootstrapping (mse.method = "bootstrap"). Partial match is supported.

nsim

Number of simulations to be performed in the bootstrapping, with a default value of 1000.

...

Arguments to be passed onto the function glm or cpglm such as contrasts or control. It is important that offset and weight should not be spe

Value

• The output is an object of class "glmReserve" that has the following components:
• call
  the matched call.
• summary
  A data frame containing the predicted loss reserve statistics. Similar to the summary statistics from MackChainLadder.
• Triangle
  The input triangle.
• FullTriangle
  The completed triangle, where empty cells in the original triangle are filled with model predictions.
• model
  The fitted GLM, a class of "glm" or "cpglm". It is most convenient to work with this component when model fit information is wanted.
• sims.par
  a matrix of the simulated parameter values in the bootstrapping.
• sims.reserve.mean
  a matrix of the simulated mean loss reserves (without the process variance) for each year in the bootstrapping.
• sims.par
  a matrix of the simulated realizations of the loss reserves (with the process variance) for each year in the bootstrapping. This can be used to summarize the predictive uncertainty of the loss reserves.

Details

This function takes an insurance loss triangle, converts it to incremental losses internally if necessary, transforms it to the long format (see as.data.frame) and fits the resulting loss data with a generalized linear model where the mean structure includes both the accident year and the development lag effects. The distributions allowed are the exponential family that admits a power variance function, that is, $V(\mu)=\mu^p$. This subclass of distributions is usually called the Tweedie distribution and includes many commonly used distributions as special cases. This function does not allow the user to specify the GLM options through the usual family argument, but instead, it uses the tweedie family internally and takes two arguments var.power and link.power through which the user still has full control of the distribution forms and link functions. The argument var.power determines which specific distribution is to be used, and link.power determines the form of the link function. When the Tweedie compound Poisson distribution 1 < p < 2 is to be used, the user has the option to specify var.power = NULL, where the variance power p will be estimated from the data using the cplm package. The bcplm function in the cplm package also has an example for the Bayesian compound Poisson loss reserving model. See details in tweedie, cpglm and bcplm. Also, the function allows certain measures of exposures to be used in an offset term in the underlying GLM. To do this, the user should not use the usual offset argument in glm. Instead, one specifies the exposure measure for each accident year through the exposure attribute of triangle. Make sure that these exposures are in the original scale (no log transformations for example), and they are in the order consistent with the accident years. If the exposure attribute is not NULL, the glmReserve function will use these exposures, link-function-transformed, in the offset term of the GLM. For example, if the link function is log, then the log of the exposure is used as the offset, not the original exposure. See the examples below. Moreover, the user MUST NOT supply the typical offset or weight as arguments in the list of additional arguments .... offset should be specified as above, while weight is not implemented (due to prediction reasons).

Two methods are available to assess the prediction error of the estimated loss reserves. One is using the analytical formula (mse.method = "formula") derived from the first-order Taylor approximation. The other is using bootstrapping (mse.method = "bootstrap") that reconstructs the triangle nsim times by sampling with replacement from the GLM (Pearson) residuals. Each time a new triangle is formed, GLM is fitted and corresponding loss reserves are generated. Based on these predicted mean loss reserves, and the model assumption about the distribution forms, realizations of the predicted values are generated via the rtweedie function. Prediction errors as well as other uncertainty measures such as quantiles and predictive intervals can be calculated based on these samples.

References

England P. and Verrall R. (1999). Analytic and bootstrap estimates of prediction errors in claims reserving. Insurance: Mathematics and Economics, 25, 281-293.

See Also

See also glm, tweedie, cpglm and MackChainLadder.

Examples

data(GenIns)
GenIns <- GenIns / 1000

# over-dispersed Poisson: reproduce ChainLadder estimates
(fit1 <- glmReserve(GenIns))
summary(fit1, type = "model")   # extract the underlying glm

# Gamma GLM:
(fit2 <- glmReserve(GenIns, var.power = 2))

# compound Poisson GLM (variance function estimated from the data):
#(fit3 <- glmReserve(GenIns, var.power = NULL))

# Now suppose we have an exposure measure
# we can put it as an offset term in the model
# to do this, use the "exposure" attribute of the 'triangle'
expos <- (7 + 1:10 * 0.4) * 100
GenIns2 <- GenIns
attr(GenIns2, "exposure") <- expos
(fit4 <- glmReserve(GenIns2))


# use bootstrapping to compute prediction error
set.seed(11)
(fit5 <- glmReserve(GenIns, mse.method = "boot"))

# compute the quantiles of the predicted loss reserves
t(apply(fit5$sims.reserve.pred, 2, quantile, 
        c(0.025, 0.25, 0.5, 0.75, 0.975)))