Bitcoin Return Dynamics Volatility and Time Series Forecasting
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We sho...
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MDPI AG
2025-06-01
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| Series: | International Journal of Financial Studies |
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| Online Access: | https://www.mdpi.com/2227-7072/13/2/108 |
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| author | Punit Anand Anand Mohan Sharan |
| author_facet | Punit Anand Anand Mohan Sharan |
| author_sort | Punit Anand |
| collection | DOAJ |
| description | Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA. |
| format | Article |
| id | doaj-art-e4a04d8bc4724e0dbab686996d3db64c |
| institution | Kabale University |
| issn | 2227-7072 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Financial Studies |
| spelling | doaj-art-e4a04d8bc4724e0dbab686996d3db64c2025-08-20T03:27:10ZengMDPI AGInternational Journal of Financial Studies2227-70722025-06-0113210810.3390/ijfs13020108Bitcoin Return Dynamics Volatility and Time Series ForecastingPunit Anand0Anand Mohan Sharan1Department of Finance and Real Estate, School of Business, Southern Connecticut State University, New Haven, CT 06515, USAFaculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B3X5, CanadaBitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.https://www.mdpi.com/2227-7072/13/2/108bitcointime seriesARMAARMA-GARCHMAERMSE |
| spellingShingle | Punit Anand Anand Mohan Sharan Bitcoin Return Dynamics Volatility and Time Series Forecasting International Journal of Financial Studies bitcoin time series ARMA ARMA-GARCH MAE RMSE |
| title | Bitcoin Return Dynamics Volatility and Time Series Forecasting |
| title_full | Bitcoin Return Dynamics Volatility and Time Series Forecasting |
| title_fullStr | Bitcoin Return Dynamics Volatility and Time Series Forecasting |
| title_full_unstemmed | Bitcoin Return Dynamics Volatility and Time Series Forecasting |
| title_short | Bitcoin Return Dynamics Volatility and Time Series Forecasting |
| title_sort | bitcoin return dynamics volatility and time series forecasting |
| topic | bitcoin time series ARMA ARMA-GARCH MAE RMSE |
| url | https://www.mdpi.com/2227-7072/13/2/108 |
| work_keys_str_mv | AT punitanand bitcoinreturndynamicsvolatilityandtimeseriesforecasting AT anandmohansharan bitcoinreturndynamicsvolatilityandtimeseriesforecasting |