报告题目：Time inconsistent optimal stopping under model ambiguity and financial applications

报 告 人：余翔 副教授 香港理工大学

邀请人：李童庆、薄立军

报告时间：2022年11月22日（周二）15：00-16：00

腾讯会议ID：286-526-952，密码：123456

报告人简介：余翔，香港理工大学副教授，2007年本科毕业于本科毕业于华中科技大学，2012年于美国德州大学奥斯汀分校获得博士学位。其主要研究领域为金融数学，应用概率，随机控制与优化，保险数学等，已在Annals of Applied Probability, Mathematical Finance, Mathematics of Operations Research, SIAM Journal of Control and Optimization等多个国际权威的概率、金融数学期刊上发表20多篇文章。

报告摘要：An unconventional approach for optimal stopping under model ambiguity is introduced. Besides ambiguity itself, we take into account how ambiguity-averse an agent is. This inclusion of ambiguity attitude, via an alpha-maxmin nonlinear expectation, renders the stopping problem time-inconsistent. We look for subgame perfect equilibrium stopping policies, formulated as fixed points of an operator. For a one-dimensional diffusion with drift and volatility uncertainty, we show that any initial stopping policy will converge to an equilibrium through a fixed-point iteration. This allows us to capture much more diverse behavior, depending on an agent's ambiguity attitude, beyond the standard worst-case analysis. In a concrete example of real options valuation under model ambiguity, all equilibrium stopping policies, as well as the best one among them, are fully characterized under appropriate conditions. It demonstrates explicitly the effect of ambiguity attitude on decision making: the more ambiguity-averse, the more eager to stop---so as to withdraw from the uncertain environment. The main result hinges on a delicate analysis of continuous sample paths in the canonical space and the capacity theory. To resolve measurability issues, a generalized measurable projection theorem, new to the literature, is also established. Joint work with Yujui Huang, University of Colorado.