Forecasting volatility with machine learning and rough volatility: example from the crypto-winter
Author:
Funder
A*Star Singapore
Ecole Polytechnique
Ministry of Education - Singapore
Iotex Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42521-024-00108-1.pdf
Reference34 articles.
1. Amirshahi, B., & Lahmiri, S. (2023). Hybrid deep learning and GARCH-family models for forecasting volatility of cryptocurrencies. Machine Learning with Applications, 12, 100465.
2. Arnosti, N., & Weinberg, S. M. (2022). Bitcoin: A natural oligopoly. Management Science, 68(7), 4755–4771.
3. Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148–151.
4. Bayer, C., Friz, P., & Gatheral, J. (2016). Pricing under rough volatility. Quantitative Finance, 16(6), 887–904.
5. Bennedsen, M., Lunde, A., & Pakkanen, M. S. (2022). Decoupling the short-and long-term behavior of stochastic volatility. Journal of Financial Econometrics, 20(5), 961–1006.
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