Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/306196 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Forecasting [ISSN:] 1099-131X [Volume:] 43 [Issue:] 7 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2024 [Pages:] 2904-2916
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state‐of‐the‐art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both in‐ and out‐of‐sample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series.
Subjects: 
forecasting
machine learning
risk measurement
time series analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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