Paper: arXiv 2408.09349

Authors: Andrea Mazzon, Peter Tankov

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 3.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a rigorous theoretical foundation combining Klibanoff’s smooth ambiguity model with optimal stopping and Bayesian learning, supported by a minimax proof. While it provides numerical examples for asset sales and energy transition divestment, it lacks empirical validation against real-world datasets.

Research Flowchart

  flowchart TD
    A[Goal: Optimal Divestment & Asset Stranding Timing] --> B[Inputs: Revenue Stream, Scenario Set & Ambiguity Aversion]
    B --> C[Framework: Smooth Model of Decision Making Klibanoff et al.]
    C --> D[Minimax Proof: Reduce Ambiguous Stopping Problem]
    D --> E[Process: Solve Standard Optimal Stopping Problems with Learning]
    E --> F[Outcomes: Asset Pricing & Optimal Divestment Strategies]

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1 of 59 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.