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Fast Lower and Upper Estimates for the Price of Constrained Multiple Exercise American Options by Single Pass Lookahead Search and Nearest-Neighbor Martingale
Nicolas Essis-Breton / Patrice Gaillardetz
algorithms / numerical methodsResource overview

Fast Lower and Upper Estimates for the Price of Constrained Multiple Exercise American Options by Single Pass Lookahead Search and Nearest-Neighbor Martingale

Paper2020

by Nicolas Essis-Breton, Patrice Gaillardetz

Tags
algorithmsnumerical methodsoptimizationoption pricing

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

Before you read

  1. Conditional Expectation

    Understand what it means to condition on current information.

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

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

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  4. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

Go further

  1. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

  2. Simulating SDEs

    Translate continuous dynamics into a numerical experiment.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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

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