Agent's Optimal Compensation Under Inflation Risk by Using Dynamic Contract Model.

IF 2.6 3区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Systems Science & Complexity Pub Date : 2021-01-01 Epub Date: 2022-01-11 DOI:10.1007/s11424-021-0008-5
Chen Fei, Weiyin Fei, Fanhong Zhang, Xiaoguang Yang
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引用次数: 1

Abstract

This paper studies the problem of principal-agent with moral hazard in continuous time. The firm's cash flow is described by geometric Brownian motion (hereafter GBM). The agent affects the drift of the firm's cash flow by her hidden effort. Meanwhile, the firm rewards the agent with corresponding compensation and equity which depend on the output. The model extends dynamic optimal contract theory to an inflation environment. Firstly, the authors obtain the dynamic equation of the firm's real cash flow under inflation by using the Itô formula. Then, the authors use the martingale representation theorem to obtain agent's continuation value process. Moreover, the authors derive the Hamilton-Jacobi-Bellman (HJB) equation of investor's value process, from which the authors derive the investors' scaled value function by solving the second-order ordinary differential equation. Comparing with He[1], the authors find that inflation risk affects the agent's optimal compensation depending on the firm's position in the market.

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通货膨胀风险下代理人最优补偿的动态契约模型。
研究了连续时间条件下具有道德风险的委托代理问题。公司的现金流用几何布朗运动(以下简称GBM)来描述。代理人通过她的隐性努力影响了公司现金流的流动。同时,企业根据产出给予代理人相应的报酬和权益。该模型将动态最优契约理论推广到通货膨胀环境。首先,利用Itô公式得到通货膨胀条件下企业实际现金流量的动态方程。然后,利用鞅表示定理得到了智能体的连续值过程。推导出投资者价值过程的Hamilton-Jacobi-Bellman (HJB)方程,并通过求解二阶常微分方程推导出投资者的标度价值函数。与He[1]相比,作者发现通货膨胀风险会影响代理的最优报酬,这取决于企业在市场中的地位。
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来源期刊
Journal of Systems Science & Complexity
Journal of Systems Science & Complexity 数学-数学跨学科应用
CiteScore
3.80
自引率
9.50%
发文量
90
审稿时长
6-12 weeks
期刊介绍: The Journal of Systems Science and Complexity is dedicated to publishing high quality papers on mathematical theories, methodologies, and applications of systems science and complexity science. It encourages fundamental research into complex systems and complexity and fosters cross-disciplinary approaches to elucidate the common mathematical methods that arise in natural, artificial, and social systems. Topics covered are: complex systems, systems control, operations research for complex systems, economic and financial systems analysis, statistics and data science, computer mathematics, systems security, coding theory and crypto-systems, other topics related to systems science.
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