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Rough volatility: fact or artefact?

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  • Rama Cont
  • Purba Das

Abstract

We investigate the statistical evidence for the use of `rough' fractional processes with Hurst exponent $H

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  • Rama Cont & Purba Das, 2022. "Rough volatility: fact or artefact?," Papers 2203.13820, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2203.13820
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    References listed on IDEAS

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    1. Fabienne Comte & Eric Renault, 1998. "Long memory in continuous‐time stochastic volatility models," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 291-323, October.
    2. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    3. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    4. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    5. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    6. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    7. Jim Gatheral & Thibault Jaisson & Mathieu Rosenbaum, 2018. "Volatility is rough," Quantitative Finance, Taylor & Francis Journals, vol. 18(6), pages 933-949, June.
    8. Lahiri, Ananya & Sen, Rituparna, 2020. "Fractional Brownian markets with time-varying volatility and high-frequency data," Econometrics and Statistics, Elsevier, vol. 16(C), pages 91-107.
    9. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    10. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, January.
    11. L. C. G. Rogers, 1997. "Arbitrage with Fractional Brownian Motion," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 95-105, January.
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    Cited by:

    1. Georgia Zournatzidou & Christos Floros, 2023. "Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices," JRFM, MDPI, vol. 16(5), pages 1-15, May.
    2. Xiyue Han & Alexander Schied, 2022. "Robust Faber--Schauder approximation based on discrete observations of an antiderivative," Papers 2211.11907, arXiv.org, revised Oct 2024.
    3. Camilla Damian & Rudiger Frey, 2023. "Detecting Rough Volatility: A Filtering Approach," Papers 2302.12612, arXiv.org.
    4. Eduardo Abi Jaber & Nathan De Carvalho, 2023. "Reconciling rough volatility with jumps," Papers 2303.07222, arXiv.org, revised Sep 2024.

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