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Reinforcement learning for options on target volatility funds
Andrea Pallavicini / Emanuele Nastasi / Roberto Daluiso / Stefano Polo
optimization / option pricingResource overview

Reinforcement learning for options on target volatility funds

Paper2021

by Andrea Pallavicini, Emanuele Nastasi, Roberto Daluiso, Stefano Polo

Tags
optimizationoption pricingoptions hedgingreinforcement 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.

Explore a related reading path

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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