Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN
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Cited by:
- Shay Kee Tan & Kok Haur Ng & Jennifer So-Kuen Chan, 2022. "Predicting Returns, Volatilities and Correlations of Stock Indices Using Multivariate Conditional Autoregressive Range and Return Models," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
- Andrei-Dragos Popescu, 2021. "Assessing Portfolio Risks Involving Bitcoin and Ethereum Using Vector Autoregressive Model," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1101-1109, December.
- Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- Fernando Moreno-Pino & Stefan Zohren, 2022. "DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions," Papers 2210.04797, arXiv.org, revised Aug 2024.
- Mamoona Zahid & Farhat Iqbal & Dimitrios Koutmos, 2022. "Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning," Risks, MDPI, vol. 10(12), pages 1-18, December.
- Anoop C V & Neeraj Negi & Anup Aprem, 2023. "Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field," Papers 2308.01013, arXiv.org.
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Keywords
bitcoin; GARCH; machine learning; recurrent neural network; volatility; risk management;All these keywords.
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