When Indemnity Insurance Fails: Parametric Coverage under Binding Budget and Risk Constraints

Battle Count: 85

The paper is primarily about insurance product design and household welfare in disaster-risk contexts. While it uses mean-variance optimization (a concept shared with portfolio theory) and compound Poisson processes (relevant to jump-diffusion models in finance), the core contribution is in insurance economics and public policy rather than trading strategy development. The actuarial mathematics and budget-constrained optimization framework could inform insurance-linked securities (ILS) pricing or catastrophe bond structuring, but this is tangential to quantitative trading.

The theoretical framework relies on closed-form solutions under specific distributional assumptions (Poisson frequency, censored exponential severity, expectation-principle pricing), making the core mathematics tractable. However, implementing the full welfare comparison requires careful handling of budget constraints, premium matching, and two-dimensional parameter surfaces. The numerical illustrations are reproducible via provided R code. The main complexity lies in the economic interpretation and calibration rather than computational difficulty.

All numerical results and figures are reproducible using R, with code available on GitHub (https://github.com/agi-lab/parametric). The paper provides complete closed-form derivations in Appendices A and B, with all moment formulas, premium expressions, and first-order conditions explicitly stated. Baseline calibration parameters are fully specified.