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Quantum computing for multidimensional option pricing: End-to-end pipeline
Julien Hok / Álvaro Leitao
derivatives / model calibrationResource overview

Quantum computing for multidimensional option pricing: End-to-end pipeline

Paper2026

by Julien Hok, Álvaro Leitao

Tags
derivativesmodel calibrationnumerical methodsoption pricingprobability

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Understand this paper

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

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

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