Pricing ambiguity in catastrophe risk insurance

Ambiguity about the probability of loss is a salient feature of catastrophe risk insurance. Evidence shows that insurers charge higher premiums under ambiguity, but that they rely on simple heuristics to do so, rather than being able to turn to pricing tools that formally link ambiguity with the insurer’s underlying economic objective. In this paper, we apply an $$\alpha$$ α -maxmin model of insurance pricing to two catastrophe model data sets relating to hurricane risk. The pricing model considers an insurer who maximises expected profit, but is sensitive to how ambiguity affects its risk of ruin. We estimate ambiguity loads and show how these depend on the insurer’s attitude to ambiguity, $$\alpha$$ α . We also compare these results with those derived from applying model blending techniques that have recently gained popularity in the actuarial profession, and show that model blending can imply relatively low aversion to ambiguity, possibly ambiguity seeking.