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Forecasting implied volatility surface with generative diffusion models
Ankush Agarwal / Chen Jin
deep learning / volatility forecastingResource overview

Forecasting implied volatility surface with generative diffusion models

Paper2025

by Ankush Agarwal, Chen Jin

Tags
deep learningvolatility forecastingvolatility surface

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Understand this paper

Suggested foundations for volatility models and surfaces and financial time series. Specialist prerequisites vary by paper.

Before you read

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

    Follow diffusion-based models and their assumptions.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  4. Linear Regression

    Read the fitted relationship and its residuals.

Go further

  1. Stochastic Volatility and the Heston Model

    Study a correlated stochastic-variance model.

  2. Reading the Implied Vol Surface — Moneyness, Term Structure, and Quoting Conventions

    Interpret prices across strikes and expiries.

  3. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

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

Explore Advanced Derivatives and Exotic Options Explore Econometrics and Financial Time Series

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