Problem Of Optimal Insurance Design Research Articles

Optimal insurance design under mean-variance preference with narrow framing

In this paper, we study an optimal insurance design problem under mean-variance criterion by considering the local gain-loss utility of the net payoff of insurance, namely, narrow framing. We extend the existing results in the literature to the case where the decision maker has mean-variance preference with a constraint on the expected utility of the net payoff of insurance, where the premium is determined by the mean-variance premium principle. We first show the existence and uniqueness of the optimal solution to the main problem studied in the paper. We find that the optimal indemnity function involves a deductible provided that the safety loading imposed on the “mean part” of the premium principle is strictly positive. Our main result shows that narrow framing indeed reduces the demand for insurance. The explicit optimal indemnity functions are derived under two special local gain-loss utility functions – the quadratic utility function and the piecewise linear utility function. As a spin-off result, the Bowley solution is also derived for a Stackelberg game between the decision maker and the insurer under the quadratic local gain-loss utility function. Several numerical examples are presented to further analyze the effects of narrow framing on the optimal indemnity function as well as the interests of both parties.

Regret-based optimal insurance design

In this paper, we investigate an optimal insurance design problem in which the insured's risk preference is of the regret-theoretical expected utility type and admissible insurance contracts are set to satisfy both the principle of indemnity and the incentive-compatible condition. When the insurance premium is calculated by the expected value principle, we show that the optimal solution for a regret-averse insured can be in the form of no insurance or partial insurance above a deductible. We obtain a necessary and sufficient condition for the optimality of no insurance and provide a numerical algorithm to derive the optimal partial insurance above a deductible. Notably, the deductible disappears if and only if the insurance premium is actuarially fair. Finally, we carry out a comparative statics analysis to examine the effect of the insured's regret coefficient on the insured's final wealth and insurance demand. When the insurance pricing is actuarially fair, we find that the increase of the regret coefficient incentivizes the insured to purchase less insurance coverage and leads to an increase in risk of the insured's final wealth. However, our numerical analysis indicates that this finding is not always true for unfair insurance pricing.

On Belief Singularity and the Optimality of Deductible Indemnity Schedules

In Ghossoub (2019), we studied a problem of optimal insurance design under belief heterogeneity. We examined the effect of the divergence in beliefs, and the singularity between the beliefs, on optimal indemnities; and we provided a closed-form characterization of optimal indemnity schedules. In this paper, we build upon the work of Ghossoub (2019) and introduce a belief singularity metric to examine the effect of the level of singularity between the beliefs on the shape of optimal indemnity schedules. Our main result shows that for any level x of belief singularity with respect to the insurer's belief Q, there exists a probability measure P exhibiting a level x of belief singularity with respect to Q, such that an optimal indemnity for a decision maker with belief P is a linear deductible with Q-probability 1, and full insurance with Q-probability 0. In this sense, we show that from the perspective of the insurer, linear deductible indemnity schedules are robust to belief singularity, which gives a possible explanation of the popularity of these contracts in practice.

Optimal insurance under rank-dependent expected utility

We re-visit the problem of optimal insurance design under Rank-Dependent Expected Utility (RDEU) examined by Bernard et al. (2015), Xu (2018), and Xu et al. (2018). Unlike the latter, we do not impose the no-sabotage condition on admissible indemnities, that is, that indemnity and retention functions be nondecreasing functions of the loss. Rather, in a departure from the aforementioned work, we impose a state-verification cost that the insurer can incur in order to verify the loss severity, hence automatically ruling out any ex post moral hazard that could otherwise arise from possible misreporting of the loss by the insured. We fully characterize the optimal indemnity schedule and discuss how our results relate to those of Bernard et al. (2015) and Xu et al. (2018). We then extend the setting by allowing for a distortion premium principle, with a distortion function that differs from that of the insured, and we provide a characterization of the optimal retention in that case.

Optimal Insurance Under Rank-Dependent Expected Utility

We re-visit the problem of optimal insurance design under Rank-Dependent Expected Utility (RDEU) examined by Bernard et al. (2015), Xu (2018), and Xu et al. (2015). Unlike the latter, we do not impose the no sabotage condition on admissible indemnities, that is, the comonotonicity of indemnity functions and retention functions with the loss. Rather, in a departure from the aforementioned work, we impose a state-verification cost that the insurer can incur in order to verify the loss severity, hence automatically ruling out any ex post moral hazard that could otherwise arise from possible misreporting of the loss by the insured. Hence, monotonicity properties of indemnification schedules become of second-order concern. We fully characterize the optimal indemnity schedule and discuss how our results relate to those of Bernard et al. (2015) and Xu et al. (2015). We then extend the setting by allowing for a distortion premium principle, with a distortion function that differs from that of the insured, and we provide a characterization of the optimal retention in that case.

Optimal Insurance Without the Nonnegativity Constraint on Indemnities: Ambiguity and Belief Heterogeneity

In Arrow's (1971) classical problem of optimal insurance design, a linear deductible schedule is optimal for an Expected-Utility (EU) maximizing decision maker (DM), if the premium depends on the indemnity's actuarial value, if the DM and the insurer share the same probabilistic beliefs about the realizations of the random loss, and under the classical constraints that, in each state of the world, the indemnity is nonnegative and does not exceed the value of the loss. Raviv (1979) re-examined Arrow's problem and concluded that the presence of a deductible is due to both the nonnegativity constraint on the indemnity and the variability in the cost of insurance. In an effort to test this statement, Gollier (1987) relaxes the nonnegativity constraint and argues that the existence of a deductible is only due to the variability in the cost of insurance. In this paper, we test whether the intuition behind Gollier's result still holds under more general preferences for the DM and the insurer. We consider a setting of ambiguity (one-sided and then two-sided) and a setting of belief heterogeneity. We drop the nonnegativity constraint and assume no cost (or a fixed cost) to the insurer, and we derive closed-form analytical solutions to the problems that we formulate. In particular, we show that an optimal indemnity (resp., retention) no longer includes a deductible provision (resp., is not equal to the realized loss). Moreover, the optimal indemnity (resp., retention) can be negative (resp., higher than the realized loss) for small values of the loss, or in case of no loss. This is in line with the intuition behind Gollier's finding in the case of belief homogeneity and no ambiguity.

Optimal insurance under rank‐dependent utility and incentive compatibility

AbstractBernard, He, Yan, and Zhou (Mathematical Finance, 25(1), 154–186) studied an optimal insurance design problem where an individual's preference is of the rank‐dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their results suffer from the unrealistic assumption that the random loss has no atom, as well as a problem of moral hazard that provides incentives for the insured to falsely report the actual loss. This paper addresses these setbacks by removing the nonatomic assumption, and by exogenously imposing the “incentive compatibility” constraint that both indemnity function and insured's retention function are increasing with respect to the loss. We characterize the optimal solutions via calculus of variations, and then apply the result to obtain explicitly expressed contracts for problems with Yaari's dual criterion and general RDU. Finally, we use numerical examples to compare the results between ours and Bernard et al.

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

In the classical expected utility framework, a problem of optimal insurance design with a premium constraint is equivalent to a problem of optimal insurance design with a minimum expected retention constraint. When the insurer has ambiguous beliefs represented by a non-additive probability measure, as in Schmeidler, this equivalence no longer holds. Recently, Amarante, Ghossoub and Phelps examined the problem of optimal insurance design with a premium constraint when the insurer has ambiguous beliefs. In particular, they showed that when the insurer is ambiguity-seeking, with a concave distortion of the insured’s probability measure, then the optimal indemnity schedule is a state-contingent deductible schedule, in which the deductible depends on the state of the world only through the insurer’s distortion function. In this paper, we examine the problem of optimal insurance design with a minimum expected retention constraint, in the case where the insurer is ambiguity-seeking. We obtain the aforementioned result of Amarante, Ghossoub and Phelps and the classical result of Arrow as special cases.

Optimal Insurance with Rank-Dependent Utility and Increasing Indemnities

Bernard et al. (2015) study an optimal insurance design problem where an individual’s preference is of the rank-dependent utility (RDU) type, and show that in general an optimal contract covers both large and small losses. However, their contracts suffer from a problem of moral hazard for paying more compensation for a smaller loss. This paper addresses this setback by exogenously imposing the constraint that both the indemnity function and the insured’s retention function be increasing with respect to the loss. We characterize the optimal solutions via calculus of variations, and then apply the result to obtain explicitly expressed contracts for problems with Yaari’s dual criterion and general RDU. Finally, we use a numerical example to compare the results between ours and that of Bernard et al. (2015).

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

In the classical Expected-Utility framework, a problem of optimal insurance design with a premium constraint is equivalent to a problem of optimal insurance design with a minimum expected retention constraint. When the insurer has ambiguous beliefs represented by a non-additive probability measure, as in Schmeidler (1989), this equivalence no longer holds. A problem of optimal insurance design with a premium constraint determined based on the insurer’s ambiguous beliefs is not equivalent to a problem of optimal insurance design with a minimum expected retention determined based on the insurer’s ambiguous beliefs. Recently, Amarante, Ghossoub, and Phelps (2014) examined the problem of optimal insurance design with a premium constraint when the insurer has ambiguous beliefs. In particular, they showed that when the insurer is ambiguity-seeking, with a concave distortion of the insured’s probability measure, then the optimal indemnity schedule is a state-contingent deductible schedule, in which the deductible depends on the state of the world only through the insurer’s distortion function. In this paper, we examine the problem of optimal insurance design with a minimum expected retention constraint, in the case where the insurer is ambiguity-seeking. We obtain the aforementioned result of Amarante, Ghossoub, and Phelps (2014) and the classical result of Arrow (1971) as special cases.

Vigilant measures of risk and the demand for contingent claims

We examine a class of utility maximization problems with a non-necessarily law-invariant utility, and with a non-necessarily law-invariant risk measure constraint. Under a consistency requirement on the risk measure that we call Vigilance, we show the existence of optimal contingent claims, and we show that such optimal contingent claims exhibit a desired monotonicity property. Vigilance is satisfied by a large class of risk measures, including all distortion risk measures and some classes of robust risk measures. As an illustration, we consider a problem of optimal insurance design where the premium principle satisfies the vigilance property, hence covering a large collection of commonly used premium principles, including premium principles that are not law-invariant. We show the existence of optimal indemnity schedules, and we show that optimal indemnity schedules are nondecreasing functions of the insurable loss.

Demand of Insurance under the Cost-of-Capital Premium Calculation Principle

We study the optimal insurance design problem. This is a risk sharing problem between an insured and an insurer. The main novelty in this paper is that we study this optimization problem under a risk-adjusted premium calculation principle for the insurance cover. This risk-adjusted premium calculation principle uses the cost-of-capital approach as it is suggested (and used) by the regulator and the insurance industry.

OPTIMAL INSURANCE DESIGN UNDER RANK‐DEPENDENT EXPECTED UTILITY

We consider an optimal insurance design problem for an individual whose preferences are dictated by the rank‐dependent expected utility (RDEU) theory with a concave utility function and an inverse‐S shaped probability distortion function. This type of RDEU is known to describe human behavior better than the classical expected utility. By applying the technique of quantile formulation, we solve the problem explicitly. We show that the optimal contract not only insures large losses above a deductible but also insures small losses fully. This is consistent, for instance, with the demand for warranties. Finally, we compare our results, analytically and numerically, both to those in the expected utility framework and to cases in which the distortion function is convex or concave.

Optimal Insurance Design Under Rank-Dependent Expected Utility

We consider an optimal insurance design problem for an individual whose preferences are dictated by the rank-dependent expected utility (RDEU) theory with a concave utility function and an inverse-S shaped probability distortion function. This type of RDEU is known to describe human behavior better than the classical expected utility. By applying the technique of quantile formulation, we solve the problem explicitly. We show that the optimal contract not only insures large losses above a deductible but also insures small losses fully. This is consistent, for instance, with the demand for warranties. Finally, we compare our results, analytically and numerically, both to those in the expected utility framework and to cases in which the distortion function is convex or concave.

Optimal Risk Sharing with Limited Liability

We solve the general problem of optimal risk sharing among a nite number of agents with limited liability. We show that the optimal allocation is characterized by endogenously determined ranks assigned to the participating agents and a hierarchical structure of risk sharing, where all agents take on risks only above the agent-speci fic thresholds determined by their ranks. When all agents have CARA utilities, linear risk sharing is optimal between two adjacent thresholds. We use our general characterization of optimal risk sharing with limited liability to solve the problem of optimal insurance design with multiple insurers. We show that the optimal thresholds, or deductibles, can be efficiently calculated through the fixed point of a contraction mapping. We then use this contraction mapping technique to derive a number of comparative statics results for optimal insurance design and its dependence on microeconomic characteristics.

An optimal insurance design problem under Knightian uncertainty

This paper solves an optimal insurance design problem in which both the insurer and the insured are subject to Knightian uncertainty about the loss distribution. The Knightian uncertainty is modeled in a multi-prior g-expectation framework. We obtain an endogenous characterization of the optimal indemnity that extends classical theorems of Arrow (Essays in the Theory of Risk Bearing. Markham, Chicago 1971) and Raviv (Am Econ Rev 69(1):84–96, 1979) in the classical situation. In the presence of Knightian uncertainty, it is shown that the optimal insurance contract is not only contingent on the realized loss but also on another source of uncertainty coming from the ambiguity.