Superior forecasting with simple AR(1) models in a low-volatility environment: evidence from the CAT bond market

Abstract In the recent literature on asset pricing, advanced machine learning methods often show better predictive quality than simple linear regression models. In this context, machine learning prediction models for bond premiums are usually based on those predictors that have proved to be particularly relevant in explanatory models. However, these models do not take into account that historical premiums of assets with particularly low premium volatilities already contain a high degree of information about future premiums. With this in mind, we consider catastrophe bonds, whose secondary market premiums exhibit low volatility, and include historical premiums in our forecasting models in addition to the usual predictors. In this way, the predictive accuracy of the linear regression is significantly increased and is comparable to that of advanced machine learning methods. Remarkably, a simple linear AR(1) model without additional predictors achieves the highest predictive performance.