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Paper / 2024SMQ / 170268005425919
Neural Term Structure of Additive Process for Option Pricing
Guixin Liu / Jimin Lin
machine learning / model calibrationResource overview

Neural Term Structure of Additive Process for Option Pricing

Paper2024

by Guixin Liu, Jimin Lin

Tags
machine learningmodel calibrationoption pricingstochastic processes

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Understand this paper

Suggested foundations for options and derivatives pricing and rates and fixed income. 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. The Bank Account, Short Rate, and the Fundamental Pricing Equation

    Understand discounting in a stochastic-rate model.

  4. DV01, Dollar Duration, and One-Factor Price Sensitivity

    Translate yield movements into first-order price risk.

Go further

  1. The Black-Scholes PDE, Formula and Implied Volatility

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

  2. Bootstrapping the Spot Curve from Market Instruments

    Build a consistent discount curve.

  3. The Vasicek Model: Mean Reversion, Affine Bond Prices, and the Gaussian Distribution

    Study a tractable mean-reverting rate model.

Explore a related reading path

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

Explore Derivatives Pricing and Computational Finance Explore Fixed Income and Interest-Rate Models

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