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Forecasting volatility with machine learning and rough volatility: example from the crypto-winter

Author

Listed:
  • Siu Hin Tang

    (National University of Singapore)

  • Mathieu Rosenbaum

    (CMAP, Ecole Polytechnique)

  • Chao Zhou

    (National University of Singapore)

Abstract

We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the “crypto-winter” in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process.

Suggested Citation

  • Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2024. "Forecasting volatility with machine learning and rough volatility: example from the crypto-winter," Digital Finance, Springer, vol. 6(4), pages 639-655, December.
    • Handle: RePEc:spr:digfin:v:6:y:2024:i:4:d:10.1007_s42521-024-00108-1
    DOI: 10.1007/s42521-024-00108-1
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    References listed on IDEAS

    as
    1. Mikkel Bennedsen & Asger Lunde & Mikko S Pakkanen, 2022. "Decoupling the Short- and Long-Term Behavior of Stochastic Volatility [Multifactor Approximation of Rough Volatility Models]," Journal of Financial Econometrics, Oxford University Press, vol. 20(5), pages 961-1006.
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    More about this item

    Keywords

    Machine learning; LSTM; Rough volatility; Quadratic rough Heston; Zumbach effect; Cryptocurrencies; Bitcoin;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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