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Paper / 2026SMQ / 189635374571030
Stochastic Policy Gradient Methods in the Uncertain Volatility Model
Gr{é}goire Loeper / Jean-Fran{\c c}ois Chassagneux / Jean-Philippe Lemor / Lokman A Abbas-Turki / Simon Sananes
monte carlo simulation / neural networksResource overview

Stochastic Policy Gradient Methods in the Uncertain Volatility Model

Paper2026

by Gr{é}goire Loeper, Jean-Fran{\c c}ois Chassagneux, Jean-Philippe Lemor, Lokman A Abbas-Turki, Simon Sananes

Tags
monte carlo simulationneural networksoption pricingreinforcement learning

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Suggested foundations for volatility models and surfaces and options and derivatives pricing. 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. The Binomial Model

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

  4. Market Model and Risk-Neutral Valuation

    Distinguish pricing probabilities from physical forecasts.

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. The Black-Scholes PDE, Formula and Implied Volatility

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

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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 Derivatives Pricing and Computational Finance

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