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Paper / 2024SMQ / 253397959334998
Optimal stopping and divestment timing under scenario ambiguity and learning
Andrea Mazzon / Peter Tankov
optimizationResource overview

Optimal stopping and divestment timing under scenario ambiguity and learning

Paper2024

by Andrea Mazzon, Peter Tankov

Tags
optimization

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Suggested foundations for optimisation and numerical methods and stochastic processes and pricing. Specialist prerequisites vary by paper.

Before you read

  1. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Conditional Expectation

    Understand what it means to condition on current information.

  4. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow Brownian dynamics and the second-order term in Itô calculus.

Go further

  1. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  2. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

  3. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

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Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Linear Algebra, Optimisation and Numerical Methods Explore Derivatives Pricing and Computational Finance

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