Optimal stopping and divestment timing under scenario ambiguity and learning

TL;DR

This paper develops a model for optimal asset divestment decisions under scenario ambiguity and learning, applying ambiguity aversion to determine optimal stopping times in uncertain future environments.

Contribution

It introduces a novel framework combining ambiguity aversion and learning into optimal stopping problems for divestment decisions, reducing complex problems to standard forms.

Findings

Minimax reduction simplifies decision problems under ambiguity.

Application to stock selling with ambiguous drift.

Application to coal plant divestment under transition uncertainty.

Abstract

Aiming to analyze the impact of environmental transition on the value of assets and on asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. Since the future scenario is unknown and the probabilities of individual prospective scenarios are ambiguous, we adopt the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal divestment decision as an optimal stopping problem with learning under ambiguity aversion. We then prove a minimax result reducing this problem to a series of standard optimal stopping problems with learning. The theory is illustrated with two examples: the problem of optimally selling a stock with ambiguous drift, and the problem of optimal divestment from a coal-fired power plant under transition scenario ambiguity.