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Title: Moral-hazard-free insurance contract design under the rank-dependent utility theory
Authors: Xu, ZQ 
Issue Date: Jun-2025
Source: Probability uncertainty and quantitative risk, June 2026, v. 10, no. 2, p. 159-190
Abstract: This paper investigates a Pareto optimal insurance contract design problem within a behavioral finance framework. In this context, the insured evaluates contracts using the rank-dependent utility (RDU, for short) theory, while the insurer applies the expected value premium principle. The analysis incorporates the incentive compatibility constraint, ensuring that the contracts, called moral-hazard-free, are free from the moral hazard issues identified in Bernard et al. [4]. Initially, the problem is formulated as a non-concave maximization problem involving Choquet expectation. It is then transformed into a quantile optimization problem and addressed using the calculus of variations method. The optimal contracts are characterized by a double-obstacle ordinary differential equation for a semi-linear second-order elliptic operator with nonlocal boundary conditions, which seems new in the financial economics literature. We present a straightforward numerical scheme and a numerical example to compute the optimal contracts. Let θ and m0 represent the relative safety loading and the mass of the potential loss at 0, respectively. We discover that every moral-hazard-free contract is optimal for infinitely many RDU-insured individuals if (Formula Presented). Conversely, certain contracts, such as the full coverage contract, are never optimal for any RDU-insured individual if (Formula Presented). Additionally, we derive all the Pareto optimal contracts when either the compensation or the retention violates the monotonicity constraint.
Keywords: Calculus of variations
Double-obstacle ordinary differential equation
Pareto optimal/efficient insurance
Probability weighting/distortion function
Quantile optimization
Rank-dependent utility theory
Publisher: AIMS Press
Journal: Probability uncertainty and quantitative risk 
ISSN: 2095-9672
EISSN: 2367-0126
DOI: 10.3934/puqr.2025008
Rights: © 2025 Shandong University and AIMS, LLC
This is the version of the article before peer review or editing, as submitted by an author to Probability, Uncertainty and Quantitative Risk, https://www.aimsciences.org/puqr. AIMS is not responsible for any errors or omissions in this version of the manuscript, or any version derived from it.
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