Stochastic Analysis and Numerics for Large Scale Dynamical Systems, with Applications

大规模动力系统的随机分析和数值及其应用

• 批准号: 1908665
• 负责人: Jianfeng Zhang
• 金额: $ 43万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908665&HistoricalAwards=false
• 关键词: Stochastic; Analysis; Numerics; Large; Scale

The increasing complexity of banking systems and other large-scale networks subject to random effects, incomplete or partial observations, and systemic risk, put increasing demands for new and efficient mathematical tools for their analysis. This award addresses three projects in the stochastic and computational analysis of "large-scale" dynamical systems, in the sense that they are set in spaces of very high dimensions or involve the interactions of a large number of participating agents in the system. The first project will focus on the development of efficient numerical schemes for partial differential equations (PDEs) with a large number of spatial dimensions. These schemes could have a significant impact on computation-oriented financial instruments, such as model-based trading algorithms involving very-large portfolios. The second project and third projects study the structures of large financial systems that are interacting in a centralized (mean-field) and non-centralized (network) manner, respectively. The results will provide tools for the measurement and management of systemic risk, particularly default contagion for large scale interbank lending markets, both for individual investors and for regulators. The award will also result in the involvement and training of graduate students and dissemination through journal publications and conferences. The first project will develop efficient Monte-Carlo methods for high-dimensional PDEs and path-dependent PDEs (PPDEs). These schemes are expected to be efficient when the dimension of the equations is allowed to be in the hundreds or higher, hence essentially breaking the notorious "curse of dimensionality" that has been baffling the numerical analysts and computer scientists for decades. The second project explore various theoretical issues as well as several practical problems in stochastic game/control theory and quantitative finance under the general framework of master equations and their variations. This project will utilize several recently developed technical tools, including the time consistency principle and the dynamic utility approach. The third project on dynamic systemic risk investigates a complex network in which the contagion effect among banks are described in a time-consistent, and non-centralized manner. By considering the state space of probability measures, the dynamical movements of networks can be described by measure-valued SDEs, while the systemic risk is characterized by certain master equations. All projects in the proposed research have direct or indirect connections to applied fields, especially to stochastic finance/actuarial sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

受随机效应、不完全或部分观察以及系统性风险影响的银行系统和其他大型网络的复杂性日益增加，这就对用于分析它们的新的、高效的数学工具提出了越来越高的要求。该奖项涉及“大规模”动力系统的随机和计算分析方面的三个项目，从这个意义上讲，它们设置在非常高维度的空间中，或者涉及系统中大量参与主体的相互作用。第一个项目将集中于开发具有大量空间维度的偏微分方程(PDE)的有效数值格式。这些方案可能对面向计算的金融工具产生重大影响，例如涉及超大型投资组合的基于模型的交易算法。第二个项目和第三个项目分别研究以集中式(平均场)和非集中式(网络)方式相互作用的大型金融系统的结构。研究结果将为衡量和管理系统性风险提供工具，尤其是对个人投资者和监管者而言，大规模银行间拆借市场的违约蔓延。该奖项还将导致研究生的参与和培训，并通过期刊出版物和会议进行传播。第一个项目将为高维偏微分方程组和路径相关偏微分方程组开发高效的蒙特卡罗方法。当方程的维度被允许在数百或更高时，这些方案预计将是有效的，从而从根本上打破几十年来困扰数值分析师和计算机科学家的臭名昭著的“维度诅咒”。第二个项目在主方程及其变体的一般框架下，探索随机博弈/控制理论和定量金融中的各种理论问题和几个实际问题。该项目将利用最近开发的几种技术工具，包括时间一致性原则和动态效用方法。第三个项目关于动态系统性风险，研究了一个复杂网络，其中银行间的传染效应以时间一致和非集中的方式描述。通过考虑概率度量的状态空间，网络的动态运动可以用度量值SDE来描述，而系统风险则可以用一定的主方程来描述。建议研究中的所有项目都与应用领域有直接或间接的联系，特别是与随机金融/精算科学有关。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Path dependent Feynman–Kac formula for forward backward stochastic Volterra integral equations

• DOI: 10.1214/21-aihp1158
• 发表时间: 2020-04
• 期刊: Annales de l'Institut Henri Poincaré, Probabilités et Statistiques
• 作者: Hanxiao Wang;J. Yong;Jianfeng Zhang

Time-Consistent Conditional Expectation Under Probability Distortion

概率畸变下的时间一致条件期望

• DOI: 10.1287/moor.2020.1101
• 发表时间: 2021
• 期刊: Mathematics of Operations Research
• 影响因子: 1.7
• 作者: Ma, Jin;Wong, Ting-Kam Leonard;Zhang, Jianfeng
• 通讯作者: Zhang, Jianfeng

Fully nonlinear stochastic and rough PDEs: Classical and viscosity solutions

全非线性随机和粗糙偏微分方程：经典和粘度解决方案

• DOI: 10.1186/s41546-020-00049-8
• 发表时间: 2020
• 期刊: Uncertainty and Quantitative Risk
• 作者: Buckdahn, Rainer;Keller, Christian;Ma, Jin;Zhang, Jianfeng
• 通讯作者: Zhang, Jianfeng

Viscosity solutions for obstacle problems on Wasserstein space

Wasserstein 空间障碍物问题的粘度解

• 发表时间: 2023
• 期刊: SIAM journal on control and optimization
• 影响因子: 2.2
• 作者: M. Talbi, N. Touzi

Optimal Investment and Dividend Strategy under Renewal Risk Model

更新风险模型下的最优投资与股利策略

• DOI: 10.1137/20m1317724
• 发表时间: 2021
• 期刊: SIAM Journal on Control and Optimization
• 影响因子: 2.2
• 作者: Bai, Lihua;Ma, Jin
• 通讯作者: Ma, Jin