  Casualty insurance-linked securities (ILS) are appealing to investors because
the underlying insurance claims, which are directly related to resulting
security performance, are uncorrelated with most other asset classes.
Conversely, casualty ILS are appealing to insurers as an efficient capital
managment tool. However, securitizing casualty insurance risk is non-trivial,
as it requires forecasting loss ratios for pools of insurance policies that
have not yet been written, in addition to estimating how the underlying losses
will develop over time within future accident years. In this paper, we lay out
a Bayesian workflow that tackles these complexities by using: (1) theoretically
informed time-series and state-space models to capture how loss ratios develop
and change over time; (2) historic industry data to inform prior distributions
of models fit to individual programs; (3) stacking to combine loss ratio
predictions from candidate models, and (4) both prior predictive simulations
and simulation-based calibration to aid model specification. Using historic
Schedule P filings, we then show how our proposed Bayesian workflow can be used
to assess and compare models across a variety of key model performance metrics
evaluated on future accident year losses.
