This course considers a wide range of stochastic reserving models for use in General Insurance, beginning with stochastic models which reproduce the traditional
chain-ladder reserve estimates. The Bornhuetter-Ferguson technique is also considered, within a Bayesian framework, which allows expert opinion to be used to
provide prior estimates of ultimate claims. The primary advantage of stochastic reserving models is the availability of measures of precision of reserve estimates,
and in this respect, attention is focused on the root mean squared error of prediction (prediction error). Of greater interest is a full predictive distribution
of possible reserve outcomes, and different methods of obtaining that distribution are described. The implementation will include the use of bootstrapping and of
MCMC methods, and computer sessions are included.
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