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Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure
Jan Pospíšil / Philipp Ziegler / Tomáš Sobotka
model calibration / monte carlo simulationResource overview

Robustness and sensitivity analyses for stochastic volatility models under uncertain data structure

Paper2019

by Jan Pospíšil, Philipp Ziegler, Tomáš Sobotka

Tags
model calibrationmonte carlo simulationoption pricingstochastic processes

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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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Explore Advanced Derivatives and Exotic Options Explore Derivatives Pricing and Computational Finance

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