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Asymptotically efficient estimation of Ergodic rough fractional Ornstein-Uhlenbeck process under continuous observations

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  • Kohei Chiba

    (Osaka University)

  • Tetsuya Takabatake

    (Hiroshima University)

Abstract

We consider the problem of asymptotically efficient estimation of drift parameters of the ergodic fractional Ornstein-Uhlenbeck process under continuous observations when the Hurst parameter $$H

Suggested Citation

  • Kohei Chiba & Tetsuya Takabatake, 2024. "Asymptotically efficient estimation of Ergodic rough fractional Ornstein-Uhlenbeck process under continuous observations," Statistical Inference for Stochastic Processes, Springer, vol. 27(1), pages 103-122, April.
    • Handle: RePEc:spr:sistpr:v:27:y:2024:i:1:d:10.1007_s11203-023-09300-3
    DOI: 10.1007/s11203-023-09300-3
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    References listed on IDEAS

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    1. Wang, Xiaohu & Xiao, Weilin & Yu, Jun, 2023. "Modeling and forecasting realized volatility with the fractional Ornstein–Uhlenbeck process," Journal of Econometrics, Elsevier, vol. 232(2), pages 389-415.
    2. Alexandre Brouste & Hiroki Masuda, 2018. "Efficient estimation of stable Lévy process with symmetric jumps," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 289-307, July.
    3. Masaaki Fukasawa & Tetsuya Takabatake & Rebecca Westphal, 2022. "Consistent estimation for fractional stochastic volatility model under high‐frequency asymptotics," Mathematical Finance, Wiley Blackwell, vol. 32(4), pages 1086-1132, October.
    4. Nualart, David & Ouknine, Youssef, 2002. "Regularization of differential equations by fractional noise," Stochastic Processes and their Applications, Elsevier, vol. 102(1), pages 103-116, November.
    5. Alexandre Brouste & Marina Kleptsyna, 2010. "Asymptotic properties of MLE for partially observed fractional diffusion system," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 1-13, April.
    6. Christian Bayer & Peter Friz & Jim Gatheral, 2016. "Pricing under rough volatility," Quantitative Finance, Taylor & Francis Journals, vol. 16(6), pages 887-904, June.
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