Andrea Mazzon

Università degli Studi di Verona

Title

Optimal stopping and divestment timing under scenario ambiguity and learning

Authors

Andrea Mazzon and Peter Tankov

Abstract

Aiming to analyze the impact of environmental transition on asset values and potential asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of one scenario among a set of possible futures. Since the future scenario is unknown and the probabilities of prospective scenarios are themselves 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. We establish a minimax result reducing this setting to a sequence of standard optimal stopping problems with learning, which makes the problem amenable to computation. The theoretical contribution is complemented by two numerical illustrations: 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, where we show how different specifications of ambiguity aversion translate into concrete differences in optimal exit times.