Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90357
Title: Optimal stopping under model ambiguity : a time-consistent equilibrium approach
Authors: Huang, YJ
Yu, X 
Issue Date: Jul-2021
Source: Mathematical finance, July 2021, v. 31, no. 3, p. 979-1012
Abstract: 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 (Formula presented.) -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 (or best-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.
Keywords: Ambiguity attitude
Equilibrium stopping policies
Generalized measurable projection theorem
Model ambiguity
Optimal stopping
Real options valuation
Time inconsistency
Publisher: Wiley-Blackwell
Journal: Mathematical finance 
ISSN: 0960-1627
DOI: 10.1111/mafi.12312
Appears in Collections:Journal/Magazine Article

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