Do Machine Learning Approaches Have the Same Accuracy in Forecasting Cryptocurrencies Volatilities?

Rayenda Brahmana

MPRA Paper from University Library of Munich, Germany

Abstract: The emergence of cryptocurrencies as digital investments drives scholars to explore their predictive prices. Intriguingly, most research focuses on its price and returns prediction using various models, leaving out the importance of persistent risk for portfolio management. This is not to mention that most research focuses only on Bitcoin, neglecting other altcoins and stablecoins. Therefore, this study comprehensively examines the cryptocurrency investment’s persistent risk from the forecasting point of view. We focus on comparing the best forecasting methods because they are vital for volatility-targeting and risk-parity in portfolio strategy. Four time-series model performances will be compared to select a suitable volatility prediction model: Machine Learning-Based GARCH, Machine Learning-Based SVR-GARCH, Neural Network, and Deep Learning. Using six different cryptocurrencies proxies: Bitcoin, Ethereum, Ripple, USD Coin, Tether, and Binance Coin, we found that ML-Based SVR-GARCH outperformed the peers in volatility forecasting. However, the prediction accuracy differences among all models are not significant. Finally, our paper provides new insights into machine learning methods’ applications in cryptocurrency market volatility prediction, which is helpful for academics, policy-makers, and investors in forming portfolio strategies.

Keywords: Volatility Forecasting; Cryptocurrencies; Bitcoin; SVR-GARCH; Neural Network; Deep Learning (search for similar items in EconPapers)
JEL-codes: C53 G17 G32 (search for similar items in EconPapers)
Date: 2022-12-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-mon, nep-pay and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:119598

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