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

Siu Hin Tang (), Mathieu Rosenbaum () and Chao Zhou ()
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Siu Hin Tang: National University of Singapore
Mathieu Rosenbaum: CMAP, Ecole Polytechnique
Chao Zhou: National University of Singapore

Digital Finance, 2024, vol. 6, issue 4, No 2, 639-655

Abstract: 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.

Keywords: Machine learning; LSTM; Rough volatility; Quadratic rough Heston; Zumbach effect; Cryptocurrencies; Bitcoin (search for similar items in EconPapers)
JEL-codes: C4 C5 C6 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42521-024-00108-1

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