  Most risk analysis models systematically underestimate the probability and
impact of catastrophic events (e.g., economic crises, natural disasters, and
terrorism) by not taking into account interconnectivity and interdependence of
risks. To address this weakness, we propose the Cascading Alternating Renewal
Process (CARP) to forecast interconnected global risks. However, assessments of
the model's prediction precision are limited by lack of sufficient ground truth
data. Here, we establish prediction precision as a function of input data size
by using alternative long ground truth data generated by simulations of the
CARP model with known parameters. We illustrate the approach on a model of
fires in artificial cities assembled from basic city blocks with diverse
housing. The results confirm that parameter recovery variance exhibits power
law decay as a function of the length of available ground truth data. Using
CARP, we also demonstrate estimation using a disparate dataset that also has
dependencies: real-world prediction precision for the global risk model based
on the World Economic Forum Global Risk Report. We conclude that the CARP model
is an efficient method for predicting catastrophic cascading events with
potential applications to emerging local and global interconnected risks.
