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Efficient simulation schemes for pricing options under the Ornstein--Uhlenbeck driven stochastic volatility model
Congxin He / Yue Kuen Kwok
monte carlo simulation / numerical methodsResource overview

Efficient simulation schemes for pricing options under the Ornstein--Uhlenbeck driven stochastic volatility model

Paper2026

by Congxin He, Yue Kuen Kwok

Tags
monte carlo simulationnumerical methodsoption pricingstochastic differential equationsstochastic processes

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Suggested foundations for volatility models and surfaces and stochastic processes and pricing. Specialist prerequisites vary by paper.

Before you read

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

    Follow Brownian dynamics and the second-order term in Itô calculus.

  2. ARCH and GARCH

    Separate conditional volatility from observed returns.

  3. Conditional Expectation

    Understand what it means to condition on current information.

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. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

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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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