Forecasting Bitcoin volatility using hybrid GARCH models with machine learning

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dc.contributor.authorZahid, Mamoona
dc.contributor.authorIqbal, Farhat
dc.contributor.authorKoutmos, Dimitrios
dc.date.accessioned2023-01-05T16:22:48Z
dc.date.available2023-01-05T16:22:48Z
dc.date.issued2022-12-13
dc.description.abstractThe time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.en_US
dc.description.sponsorshipThis research received no external funding.en_US
dc.identifier.citationZahid, Mamoona, Farhat Iqbal, and Dimitrios Koutmos. 2022. Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning. Risks 10: 237. https://doi.org/10.3390/risks 10120237en_US
dc.identifier.doihttps://doi.org/10.3390/risks10120237
dc.identifier.urihttps://hdl.handle.net/1969.6/94855
dc.language.isoen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectvolatilityen_US
dc.subjectBitcoinen_US
dc.subjectmachine learningen_US
dc.subjectGARCHen_US
dc.subjectrecurrent neural networksen_US
dc.titleForecasting Bitcoin volatility using hybrid GARCH models with machine learningen_US
dc.typeArticleen_US

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