Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning

Mamoona Zahid, Farhat Iqbal and Dimitrios Koutmos ()
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Mamoona Zahid: Department of Statistics, University of Balochistan, Quetta 87300, Pakistan
Farhat Iqbal: Department of Statistics, University of Balochistan, Quetta 87300, Pakistan
Dimitrios Koutmos: Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA

Risks, 2022, vol. 10, issue 12, 1-18

Abstract: The 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.

Keywords: volatility; Bitcoin; machine learning; GARCH; recurrent neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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