Seminario Optimal stopping and divestment timing under scenario ambiguity and learning

Relatore: Andrea Mazzon (University of Verona)

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. This is a joint work with Peter Tankov from ENSAE, Paris.