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Convex duality for Epstein-Zin stochastic differential utility
Matoussi, Anis / Xing, Hao
convex optimization / optimizationResource overview

Convex duality for Epstein-Zin stochastic differential utility

Paper2018

by Matoussi, Anis, Xing, Hao

Tags
convex optimizationoptimization

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

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  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.

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  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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