An Application of Geometric Riemannian Pricing Algorithm for Valuing Catastrophe Bonds in Incomplete Markets: Market-Consistent Financial Entropy and the Split of Frequency and Severity Risk Premia

Abstract

This paper presents a framework for pricing catastrophe (CAT) bonds in incomplete markets through Riemannian geometric optimization of financial entropy. We formulate pricing-measure selection as a maximum financial entropy problem on a finite-dimensional manifold of equivalent measures, parameterized by the non-hedgeable market prices of catastrophe frequency and severity risk, with discounting fixed by the default-free term structure. Since unconstrained entropy maximization recovers the physical measure, we incorporate observed secondary-market spreads, both pooled and date by date. For a compound Poisson-generalized Pareto loss model with Wang distortion, we derive closed-form expressions for financial entropy and the Riemannian metric. We show that CAT bond prices depend on frequency and severity premia primarily through the risk-neutral trigger intensity, making the two premia unidentifiable from market data alone; the entropy criterion resolves this ambiguity. Using a hypothetical earthquake CAT bond calibrated to quarterly spreads of Golden State Re II Ltd. (Series 2014-1), we obtain risk-neutral trigger intensities of 6.58 to 10.45 times the physical intensity (9.20 pooled and 18.26 at the end of 2015). The model-implied risk multiple aligns with the market spread-to-expected-loss ratio (8.87 versus 8.81 at issuance). The minimal-entropy measure attributes 53.5% to 63.7% of the log risk multiple to frequency risk, reflecting the selection principle rather than market identification. Finally, equating the coupon with the quoted spread understates the risk multiple by approximately half.