Parametric insurance: what the theory gets wrong about practice
Parametric insurance: what the theory gets wrong about practice A big strand of recent academic work models parametric insurance as a very general indemnity function of many indices – weather variables, satellite metrics, macro factors, you name it. Mathematically, that’s elegant: You get a rich state space You can define an optimal contract in terms of basis risk You can use all the toys of convex / linear / even non-convex optimisation and ML But as a description of real parametric products, this framing is often misleading . It abstracts away the very features that make parametric insurance commercially viable. Let me highlight a few reasons why. 1. The superpower of parametrics is simplicity of the trigger In practice, parametric contracts almost always have: One simple trigger (or at most a very simple double-trigger), and A transparent, discrete payout schedule Not a high-dimensional function of 7 climate indices + 3 macro factors + a latent neural-net feature. ...