New Problems in Stochastic Control Motivated by Mathematical Finance

数学金融引发的随机控制新问题

• 批准号: 1613170
• 负责人: Erhan Bayraktar
• 金额: $ 33.92万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1613170&HistoricalAwards=false
• 关键词: New; Problems; Stochastic; Control; Motivated

Roughly speaking, game theory aims to determine the best that two parties can do, separately or together, in contests in which each attempts to achieve an objective that may be at least partially contradictory to that of the other. Games (contests) involving more than two parties are more complicated, but the analysis of large-population games improves our understanding of complex systems in finance, economics, and engineering that are otherwise difficult to analyze. On the other hand, improved understanding of model uncertainty in finance leads to a better management of risk. This research project explores mathematical questions in these areas and aims to develop new mathematical tools, inspired by applications in mathematical finance. Graduate students and post-doctoral researchers are directly involved in the work. There have been some exciting developments in stochastic control inspired by finance and economics in recent years: Financial modeling with model uncertainty led to some new problems in optimal transport theory (namely the martingale optimal transport). The super-hedging problems led to the geometric dynamic programming principle, and the analysis of Nash equilibria of games with a large number of players each having a very little influence on the overall system led to the theory of mean field games. This research project aims to contribute to these developments by providing some new mathematical tools and studying some new questions motivated by applications. The project aims to further advance understanding of financial mathematics with model uncertainty, the geometric dynamic programming principle, randomization approaches to stochastic control, and mean field type control problems and mean field games.

粗略地说，博弈论的目的是确定在双方试图实现至少部分与另一方的目标相矛盾的目标的竞争中，双方单独或共同能做的最好的事情。涉及双方以上的博弈(竞赛)更加复杂，但对人口众多的博弈的分析提高了我们对金融、经济和工程中的复杂系统的理解，否则这些复杂系统很难分析。另一方面，更好地理解金融中的模型不确定性会带来更好的风险管理。这项研究项目探索这些领域的数学问题，旨在开发新的数学工具，灵感来自于在数学金融中的应用。研究生和博士后研究人员直接参与了这项工作。近年来，在金融学和经济学的启发下，随机控制有了一些令人振奋的发展：模型不确定的金融建模导致了最优运输理论中的一些新问题(即鞅最优运输)。超套期保值问题引出了几何动态规划原理，而对大量参与者博弈的纳什均衡的分析则导致了平均场博弈理论的产生。本研究项目旨在通过提供一些新的数学工具和研究一些由应用引发的新问题来促进这些发展。该项目旨在通过模型不确定性、几何动态规划原理、随机控制的随机化方法、平均场类型控制问题和平均场博弈来进一步加深对金融数学的理解。