Optimal stopping and divestment timing under scenario ambiguity and learning

2024-08-18

AI Summary

This paper addresses how economic agents dynamically optimize the timing of asset divestment or exit under environmental transition, confronting both uncertainty and ambiguity—particularly regarding future climate policy pathways—that exacerbate stranded-asset risk. Methodologically, it innovatively embeds the Klibanoff–Marinacci–Mukerji smooth ambiguity model into a Bayesian learning–augmented optimal stopping framework, proving that the resulting problem reduces to a sequence of standard learning-based optimal stopping problems solved via minimax optimization. Integrating ambiguity-sensitive decision theory, stochastic control, and minimax optimization, the approach yields the first tractable, computationally implementable dynamic decision rules for equity divestment (under drift ambiguity) and coal-power asset retirement (under scenario ambiguity). It further quantifies the marginal effect of ambiguity aversion on exit timing. The framework significantly enhances both theoretical rigor and practical applicability of asset disposition decisions under transition risk.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• Analyzing environmental transition impacts on asset stranding value
• Modeling optimal divestment timing under ambiguous future scenarios
• Solving optimal stopping problems with ambiguity aversion and learning

Innovation

• Optimal stopping with ambiguity aversion model
• Minimax reduction to standard stopping problems
• Learning under scenario ambiguity for divestment