One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.

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比特币回报波动率预测：GARCH 和 RNN 的比较研究

比特币的显着特征之一是其极端的波动性。比特币波动率的建模和预测对于比特币投资者的决策分析和风险管理至关重要。然而，之前对比特币波动性的大多数研究都是建立在计量经济学模型上的。使用机器学习算法进行比特币波动率预测的研究仍然很少。在这项研究中，传统的计量经济学模型和机器学习模型都用于预测比特币的回报波动和风险价值。本研究的目的是比较它们在预测准确性和风险管理效率方面的样本外表现。结果表明，RNN 在平均预测性能上优于 GARCH 和 EWMA。然而，它在捕捉比特币市场的极端事件方面效率较低。而且，RNN 在风险价值预测中表现不佳，表明它在解释极端波动性方面不能像计量经济学模型那样有效。这项研究提出了一种比特币波动率分析的替代方法，并为经济研究人员将机器学习方法应用于波动较小的金融市场条件提供了更多动力。同时，它还表明机器学习方法并不总是比计量经济学模型更先进，这与普遍看法相反。这项研究提出了一种比特币波动率分析的替代方法，并为经济研究人员将机器学习方法应用于波动较小的金融市场条件提供了更多动力。同时，它还表明机器学习方法并不总是比计量经济学模型更先进，这与普遍看法相反。这项研究提出了一种比特币波动率分析的替代方法，并为经济研究人员将机器学习方法应用于波动较小的金融市场条件提供了更多动力。同时，它还表明机器学习方法并不总是比计量经济学模型更先进，这与普遍看法相反。