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Rough volatility, path-dependent PDEs and weak rates of convergence

Author

Listed:
  • Ofelia Bonesini
  • Antoine Jacquier
  • Alexandre Pannier
Abstract
In the setting of stochastic Volterra equations, and in particular rough volatility models, we show that conditional expectations are the unique classical solutions to path-dependent PDEs. The latter arise from the functional It\^o formula developed by [Viens, F., & Zhang, J. (2019). A martingale approach for fractional Brownian motions and related path dependent PDEs. Ann. Appl. Probab.]. We then leverage these tools to study weak rates of convergence for discretised stochastic integrals of smooth functions of a Riemann-Liouville fractional Brownian motion with Hurst parameter $H \in (0,1/2)$. These integrals approximate log-stock prices in rough volatility models. We obtain weak error rates of order 1 if the test function is quadratic and of order $H+1/2$ for smooth test functions.

Suggested Citation

  • Ofelia Bonesini & Antoine Jacquier & Alexandre Pannier, 2023. "Rough volatility, path-dependent PDEs and weak rates of convergence," Papers 2304.03042, arXiv.org.
    • Handle: RePEc:arx:papers:2304.03042
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    References listed on IDEAS

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    Cited by:

    1. Peter Bank & Christian Bayer & Peter K. Friz & Luca Pelizzari, 2023. "Rough PDEs for local stochastic volatility models," Papers 2307.09216, arXiv.org.
    2. Alexandre Pannier, 2023. "Path-dependent PDEs for volatility derivatives," Papers 2311.08289, arXiv.org, revised Jan 2024.
    3. Antoine Jacquier & Mugad Oumgari, 2023. "Interest rate convexity in a Gaussian framework," Papers 2307.14218, arXiv.org, revised Mar 2024.

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