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

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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.

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Data availability

All data used are publicly available on binance.com.

Notes

  1. Mccrank, J. (2022, February 14). Wall St Week Ahead Crypto investors face more uncertainty after rocky start to 2022. Reuters. https://www.reuters.com/business/finance/wall-st-week-ahead-crypto-investors-face-more-uncertainty-after-rocky-start-2022-2022-02-11/.

  2. Harrison, E. (2022, May 10). The crypto-winter is here. Bloomberg.com. https://www.bloomberg.com/news/newsletters/2022-05-10/the-crypto-winter-is-here.

  3. Howcroft, E. (2022, June 13). Cryptocurrency market value slumps under $1 trillion. Reuters. https://www.reuters.com/business/finance/cryptocurrency-market-value-slumps-under-1-trillion-2022-06-13/.

  4. Coingecko. (2023). 2022 Annual Crypto Industry Report. CoinGecko. https://www.coingecko.com/research/publications/2022-annual-crypto-report.

  5. More about USDT can be found at https://tether.to/en/how-it-works.

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Acknowledgements

Siu Hin Tang is supported by the SINGA award by A*Star Singapore. Mathieu Rosenbaum is supported by the École Polytechnique’s chairs Deep finance and statistics and Machine learning and systematic methods. Chao Zhou is supported by the Ministry of Education in Singapore under the MOE AcRF grants A-0004255-00-00, A-0004273-00-00, A-0004589-00-00 and by Iotex Foundation Ltd under the grant A-8001180-00-00.

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Authors and Affiliations

  1. Department of Mathematics, National University of Singapore, Singapore, Singapore

    Siu Hin Tang

  2. CMAP, Ecole Polytechnique, Palaiseau, France

    Mathieu Rosenbaum

  3. Department of Mathematics and Risk Management Institute, National University of Singapore, Singapore, Singapore

    Chao Zhou

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  1. Siu Hin Tang
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  2. Mathieu Rosenbaum
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  3. Chao Zhou
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S.H.T. prepared the data and conducted the numerical experiments. All authors wrote and reviewed the manuscript.

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Correspondence to Siu Hin Tang.

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Tang, S.H., Rosenbaum, M. & Zhou, C. Forecasting volatility with machine learning and rough volatility: example from the crypto-winter. Digit Finance (2024). https://doi.org/10.1007/s42521-024-00108-1

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