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Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate
Andrea Molent / Antonino Zanette / Ludovic Goudenège
interest rates / machine learningResource overview

Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate

Paper2021

by Andrea Molent, Antonino Zanette, Ludovic Goudenège

Tags
interest ratesmachine learningnumerical methodsstochastic processes

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Suggested foundations for rates and fixed income and volatility models and surfaces. Specialist prerequisites vary by paper.

Before you read

  1. The Bank Account, Short Rate, and the Fundamental Pricing Equation

    Understand discounting in a stochastic-rate model.

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

    Translate yield movements into first-order price risk.

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

    Follow diffusion-based models and their assumptions.

  4. ARCH and GARCH

    Separate conditional volatility from observed returns.

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  1. Bootstrapping the Spot Curve from Market Instruments

    Build a consistent discount curve.

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

    Study a tractable mean-reverting rate model.

  3. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

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