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Paper / 2023SMQ / 281141337886326
Exponential Utility Maximization in a Discrete Time Gaussian Framework
Or Zuk / Yan Dolinsky
mathematical finance / optimizationResource overview

Exponential Utility Maximization in a Discrete Time Gaussian Framework

Paper2023

by Or Zuk, Yan Dolinsky

Tags
mathematical financeoptimization

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Suggested foundations for options and derivatives pricing and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. The Binomial Model

    Build replication and risk-neutral probabilities in a tractable model.

  2. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

  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. The Black-Scholes PDE, Formula and Implied Volatility

    Connect the pricing equation, closed-form value and implied volatility.

  2. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  3. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

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

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