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The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles

Author

Listed:
  • Eduardo Abi Jaber

    (Xiaoyuan)

  • Camille Illand

    (Xiaoyuan)

  • Shaun

    (Xiaoyuan)

  • Li

Abstract

The quintic Ornstein-Uhlenbeck volatility model is a stochastic volatility model where the volatility process is a polynomial function of degree five of a single Ornstein-Uhlenbeck process with fast mean reversion and large vol-of-vol. The model is able to achieve remarkable joint fits of the SPX-VIX smiles with only 6 effective parameters and an input curve that allows to match certain term structures. We provide several practical specifications of the input curve, study their impact on the joint calibration problem and consider additionally time-dependent parameters to help achieve better fits for longer maturities going beyond 1 year. Even better, the model remains very simple and tractable for pricing and calibration: the VIX squared is again polynomial in the Ornstein-Uhlenbeck process, leading to efficient VIX derivative pricing by a simple integration against a Gaussian density; simulation of the volatility process is exact; and pricing SPX products derivatives can be done efficiently and accurately by standard Monte Carlo techniques with suitable antithetic and control variates.

Suggested Citation

  • Eduardo Abi Jaber & Camille Illand & Shaun & Li, 2022. "The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles," Papers 2212.10917, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2212.10917
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    References listed on IDEAS

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    Cited by:

    1. Eduardo Abi Jaber & Nathan De Carvalho, 2023. "Reconciling rough volatility with jumps," Papers 2303.07222, arXiv.org, revised Sep 2024.
    2. Eduardo Abi Jaber & Shaun & Li & Xuyang Lin, 2024. "Fourier-Laplace transforms in polynomial Ornstein-Uhlenbeck volatility models," Papers 2405.02170, arXiv.org.
    3. Eduardo Abi Jaber & Shaun & Li, 2024. "Volatility models in practice: Rough, Path-dependent or Markovian?," Papers 2401.03345, arXiv.org.

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