Optimal Ratcheting of Dividend Payout Under Brownian Motion Surplus

IF 2.2 2区 数学 Q2 AUTOMATION & CONTROL SYSTEMS SIAM Journal on Control and Optimization Pub Date : 2024-09-10 DOI:10.1137/23m159250x
Chonghu Guan, Zuo Quan Xu
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Abstract

SIAM Journal on Control and Optimization, Volume 62, Issue 5, Page 2590-2620, October 2024.
Abstract. This paper is concerned with a long-standing optimal dividend payout problem subject to the so-called ratcheting constraint, that is, the dividend payout rate shall be nondecreasing over time and is thus self-path-dependent. The surplus process is modeled by a drifted Brownian motion process and the aim is to find the optimal dividend ratcheting strategy to maximize the expectation of the total discounted dividend payouts until the ruin time. Due to the self-path-dependent control constraint, the standard control theory cannot be directly applied to tackle the problem. The related Hamilton–Jacobi–Bellman (HJB) equation is a new type of variational inequality. In the literature, it is only shown to have a viscosity solution, which is not strong enough to guarantee the existence of an optimal dividend ratcheting strategy. This paper proposes a novel partial differential equation method to study the HJB equation. We not only prove the existence and uniqueness of the solution in some stronger functional space, but also prove the strict monotonicity, boundedness, and [math]-smoothness of the dividend ratcheting free boundary. Based on these results, we eventually derive an optimal dividend ratcheting strategy, and thus solve the open problem completely. Economically speaking, we find that if the surplus volatility is above an explicit threshold, then one should pay dividends at the maximum rate, regardless of the surplus level. Otherwise, by contrast, the optimal dividend ratcheting strategy relies on the surplus level and one should only ratchet up the dividend payout rate when the surplus level touches the dividend ratcheting free boundary. Moreover, our numerical results suggest that one should invest in those companies with stable dividend payout strategies since their income rates should be higher and volatility rates smaller.
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布朗运动盈余条件下的最优梯度股利分配
SIAM 控制与优化期刊》第 62 卷第 5 期第 2590-2620 页,2024 年 10 月。 摘要本文关注的是一个长期存在的最优股利支付问题,该问题受到所谓的梯度约束,即股利支付率应随时间不递减,因此是自路径依赖的。盈余过程以漂移布朗运动过程为模型,目的是找到最优的股息递增策略,以最大化直至毁灭时间的总贴现股息支付的期望值。由于存在自路径依赖控制约束,标准控制理论无法直接用于解决该问题。相关的汉密尔顿-雅各比-贝尔曼(HJB)方程是一种新型的变分不等式。在文献中,它只被证明有一个粘性解,而这个粘性解并不足以保证最优红利棘轮策略的存在。本文提出了一种新的偏微分方程方法来研究 HJB 方程。我们不仅证明了在某个更强的函数空间中解的存在性和唯一性,还证明了股息梯度自由边界的严格单调性、有界性和[math]-光滑性。在这些结果的基础上,我们最终推导出一个最优的股息梯度策略,从而彻底解决了这个开放性问题。从经济学角度看,我们发现如果盈余波动性高于一个明确的临界值,那么无论盈余水平如何,都应该以最大比率支付股利。与此相反,最优的股利递增策略依赖于盈余水平,只有当盈余水平触及股利递增自由边界时,才应提高股利支付率。此外,我们的数值结果表明,人们应该投资于那些具有稳定股利支付策略的公司,因为它们的收益率应该更高,波动率应该更小。
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来源期刊
CiteScore
4.00
自引率
4.50%
发文量
143
审稿时长
12 months
期刊介绍: SIAM Journal on Control and Optimization (SICON) publishes original research articles on the mathematics and applications of control theory and certain parts of optimization theory. Papers considered for publication must be significant at both the mathematical level and the level of applications or potential applications. Papers containing mostly routine mathematics or those with no discernible connection to control and systems theory or optimization will not be considered for publication. From time to time, the journal will also publish authoritative surveys of important subject areas in control theory and optimization whose level of maturity permits a clear and unified exposition. The broad areas mentioned above are intended to encompass a wide range of mathematical techniques and scientific, engineering, economic, and industrial applications. These include stochastic and deterministic methods in control, estimation, and identification of systems; modeling and realization of complex control systems; the numerical analysis and related computational methodology of control processes and allied issues; and the development of mathematical theories and techniques that give new insights into old problems or provide the basis for further progress in control theory and optimization. Within the field of optimization, the journal focuses on the parts that are relevant to dynamic and control systems. Contributions to numerical methodology are also welcome in accordance with these aims, especially as related to large-scale problems and decomposition as well as to fundamental questions of convergence and approximation.
期刊最新文献
Local Exact Controllability of the One-Dimensional Nonlinear Schrödinger Equation in the Case of Dirichlet Boundary Conditions Backward Stochastic Differential Equations with Conditional Reflection and Related Recursive Optimal Control Problems Logarithmic Regret Bounds for Continuous-Time Average-Reward Markov Decision Processes Optimal Ratcheting of Dividend Payout Under Brownian Motion Surplus An Optimal Spectral Inequality for Degenerate Operators
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