Pareto optimal moral-hazard-free insurance contracts in behavioral finance framework

TL;DR

This paper develops a framework for finding Pareto optimal insurance contracts that are free of moral hazard in a behavioral finance setting, using rank-dependent utility and advanced mathematical techniques.

Contribution

It introduces a novel approach to characterize and compute moral-hazard-free Pareto optimal insurance contracts within a behavioral finance model using differential equations and numerical methods.

Findings

Contracts are optimal for many RDU insureds if safety loading is below a threshold.

Full coverage contracts are never optimal if safety loading exceeds a certain ratio.

The paper derives all Pareto optimal contracts under specific monotonicity conditions.

Abstract

This paper investigates Pareto optimal (PO, for short) insurance contracts in a behavioral finance framework, in which the insured evaluates contracts by the rank-dependent utility (RDU) theory and the insurer by the expected value premium principle. The incentive compatibility constraint is taken into account, so the contracts are free of moral hazard. The problem is initially formulated as a non-concave maximization problem involving Choquet expectation, then turned into a quantile optimization problem and tackled by calculus of variations method. The optimal contracts are expressed by a double-obstacle ordinary differential equation for a semi-linear second-order elliptic operator with nonlocal boundary conditions. We provide a simple numerical scheme as well as a numerical example to calculate the optimal contracts. Let $\theta$ and $m_0$ denote the relative safety loading and the mass of the potential loss at 0. We find that every moral-hazard-free contract is optimal for infinitely many RDU insureds if $0<\theta<\frac{m_0}{1-m_0}$; by contrast, some contracts such as the full coverage contract are never optimal for any RDU insured if $\theta>\frac{m_0}{1-m_0}$. We also derive all the PO contracts when either the compensations or the retentions loss monotonicity.