2005Review of Financial StudiesRequires access

Optimal Contracts Under Adverse Selection and Moral Hazard: A Continuous-Time Approach

Jaeyoung Sung

Open publisher page 69 citations

Abstract

This article presents a continuous-time agency model in the presence of adverse selection and moral hazard with a risk-averse agent and a risk-neutral principal. Under the model setup, we show that the optimal controls are constant over time, and thus the optimal menu consists of contracts that are linear in the final outcome. We also show that when a moral hazard problem adds to an adverse selection problem, the monotonicity condition well known in the pure adverse selection literature needs to be modified to ensure the incentive compatibility for information revelation. The model is applied to a few managerial compensation problems involving managerial project selection and capital budgeting decisions. We argue that in the third-best world, the relationship between the volatility of the outcome and the sensitivity of the contract depends on interactions between the managerial cost and the firm’s production functions. Contrary to conventional wisdom, sometimes the higher the volatility, the higher the sensitivity of the contract. The firm receiving good news sometimes chooses safer projects or invests less than it does with bad news. We also examine the effects of the observability of the volatility on corporate investment decisions.

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This article presents a continuous-time agency model in the presence of adverse selection and moral hazard with a risk-averse agent and a risk-neutral principal. Under the model setup, we show that the optimal controls are constant over time, and thus the optimal menu consists of contracts that are linear in the final outcome. We also show that when a moral hazard problem adds to an adverse selection problem, the monotonicity condition well known in the pure adverse selection literature needs to be modified to ensure the incentive compatibility for information revelation. The model is applied to a few managerial compensation problems involving managerial project selection and capital budgeting decisions. We argue that in the third-best world, the relationship between the volatility of the outcome and the sensitivity of the contract depends on interactions between the managerial cost and the firm’s production functions. Contrary to conventional wisdom, sometimes the higher the volatility, the higher the sensitivity of the contract. The firm receiving good news sometimes chooses safer projects or invests less than it does with bad news. We also examine the effects of the observability of the volatility on corporate investment decisions.

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Available abstract

This article presents a continuous-time agency model in the presence of adverse selection and moral hazard with a risk-averse agent and a risk-neutral principal. Under the model setup, we show that the optimal controls are constant over time, and thus the optimal menu consists of contracts that are linear in the final outcome. We also show that when a moral hazard problem adds to an adverse selection problem, the monotonicity condition well known in the pure adverse selection literature needs to be modified to ensure the incentive compatibility for information revelation. The model is applied to a few managerial compensation problems involving managerial project selection and capital budgeting decisions. We argue that in the third-best world, the relationship between the volatility of the outcome and the sensitivity of the contract depends on interactions between the managerial cost and the firm’s production functions. Contrary to conventional wisdom, sometimes the higher the volatility, the higher the sensitivity of the contract. The firm receiving good news sometimes chooses safer projects or invests less than it does with bad news. We also examine the effects of the observability of the volatility on corporate investment decisions.

Key concepts: Moral hazard, Adverse selection, Selection (genetic algorithm), Hazard, Actuarial science, Economics, Computer science, Incentive

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