ADLP - Accident and Development Period Adjusted Linear Pools for
Actuarial Stochastic Reserving
Loss reserving generally focuses on identifying a single
model that can generate superior predictive performance.
However, different loss reserving models specialise in
capturing different aspects of loss data. This is recognised in
practice in the sense that results from different models are
often considered, and sometimes combined. For instance,
actuaries may take a weighted average of the prediction
outcomes from various loss reserving models, often based on
subjective assessments. This package allows for the use of a
systematic framework to objectively combine (i.e. ensemble)
multiple stochastic loss reserving models such that the
strengths offered by different models can be utilised
effectively. Our framework is developed in Avanzi et al.
(2023). Firstly, our criteria model combination considers the
full distributional properties of the ensemble and not just the
central estimate - which is of particular importance in the
reserving context. Secondly, our framework is that it is
tailored for the features inherent to reserving data. These
include, for instance, accident, development, calendar, and
claim maturity effects. Crucially, the relative importance and
scarcity of data across accident periods renders the problem
distinct from the traditional ensemble techniques in
statistical learning. Our framework is illustrated with a
complex synthetic dataset. In the results, the optimised
ensemble outperforms both (i) traditional model selection
strategies, and (ii) an equally weighted ensemble. In
particular, the improvement occurs not only with central
estimates but also relevant quantiles, such as the 75th
percentile of reserves (typically of interest to both insurers
and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A
(2023) "Ensemble distributional forecasting for insurance loss
reserving" <doi:10.48550/arXiv.2206.08541>.
