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Statistical inference for rough volatility: Minimax Theory

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Listed:
  • Carsten Chong
  • Marc Hoffmann
  • Yanghui Liu
  • Mathieu Rosenbaum
  • Gr'egoire Szymanski

Abstract

Rough volatility models have gained considerable interest in the quantitative finance community in recent years. In this paradigm, the volatility of the asset price is driven by a fractional Brownian motion with a small value for the Hurst parameter $H$. In this work, we provide a rigorous statistical analysis of these models. To do so, we establish minimax lower bounds for parameter estimation and design procedures based on wavelets attaining them. We notably obtain an optimal speed of convergence of $n^{-1/(4H+2)}$ for estimating $H$ based on n sampled data, extending results known only for the easier case $H>1/2$ so far. We therefore establish that the parameters of rough volatility models can be inferred with optimal accuracy in all regimes.

Suggested Citation

  • Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Minimax Theory," Papers 2210.01214, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2210.01214
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    References listed on IDEAS

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    Cited by:

    1. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Central limit theorems," Papers 2210.01216, arXiv.org, revised Jul 2023.
    2. Xiyue Han & Alexander Schied, 2023. "Estimating the roughness exponent of stochastic volatility from discrete observations of the realized variance," Papers 2307.02582, arXiv.org, revised Aug 2023.
    3. Saad Mouti, 2023. "Rough volatility: evidence from range volatility estimators," Papers 2312.01426, arXiv.org.

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