This is the source code for the hirem
package, which is currently under development.
To install hirem
from GitHub you will need devtools
:
install.packages('devtools')
devtools::install_github('jonascrevecoeur/hirem')
The hirem
package offers tools for implementing hierarchical reserving models, introduced in the paper Crevecoeur, J., Robben J., Antonio, K., A hierarchical reserving model for reported non-life insurance claims, (2021).
The hirem
package constructs hierarchical reserving models by sequentially adding new layers. An example of a three component hierarchical model:
require(hirem)
data("reserving_data")
model <- hirem(reserving_data %>% dplyr::filter(calendar_year <= 6)) %>%
layer_glm('close', binomial(link = logit)) %>%
layer_glm('payment', binomial(link = logit)) %>%
layer_glm('size', Gamma(link = log),
filter = function(data){data$payment == 1})
model <- fit(model,
close = 'close ~ factor(development_year)',
payment = 'payment ~ close + factor(development_year)',
size = 'size ~ close + factor(development_year)')
The package is still under development. Currently supported layers are:
layer_glm
: Estimates a layer using a generalized linear modellayer_gbm
: Estimates a layer using a gradient boosting model
Supported distributions are:
binomial
gaussian
gamma
To use the gamma
distribution in layer_gbm
you require an experimental version of the package gbm
that implements the gamma
distribution. See
https://github.com/harrysouthworth/gbm.
Use the links below to view examples and vignettes in your current browser:
Vignette:
Connection between aggregate and individual reserving:
Principles of the simulation machine:
Application on a simulated portfolio: