Because of the very unstable and unpredictable character of bitcoin assets, forecasting market volatility remains a difficult challenge for investors and risk managers. Traditional econometric models, such as GARCH, have been widely utilised for volatility forecasting, but they typically fail to capture the complex nonlinear patterns and abrupt market movements that occur with cryptocurrencies. Deep learning approaches have grown in prominence in recent years as a result of their improved capacity to handle complicated data patterns. However, there is still a paucity of research that properly compares the forecasting effectiveness of deep learning models to older approaches while also taking into account computing efficiency. The GARCH (1,1), EGARCH, and TGARCH models, together with deep learning architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are compared in this study in an effort to bridge this gap. From January 2020 to December 2023, the study uses a walk-forward validation technique to analyse daily Bitcoin and Ethereum price data in order to arrive at a trustworthy and accurate assessment. The findings reveal that deep learning models beat classic econometric techniques in prediction accuracy. The LSTM model decreases RMSE by roughly 18.2%, while the GRU model produces an MAE that is approximately 22.4% lower than the best-performing GARCH model. Traditional models, on the other hand, have a significant computational efficiency advantage since they require almost 200 times less training time than deep learning approaches. Statistical testing demonstrate that the performance differences are very significant at the 1% confidence level. Overall, this study provides researchers and practitioners with practical advice on how to select appropriate volatility prediction models based on their accuracy, interpretability, and computational resource requirements, as well as a clear benchmark comparison of traditional and contemporary forecasting techniques.
include deep learning models cryptocurrency, bitcoin, ethereum, GARCH models, long short-term memory (LSTM), volatility predictions, and comparative analysis.
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