Next-Day Bitcoin Price Forecast
Ziaul Haque Munim (),
Mohammad Hassan Shakil () and
Ilan Alon ()
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Ziaul Haque Munim: School of Business and Law, University of Agder, 4630 Kristiansand, Norway
Mohammad Hassan Shakil: Taylor’s Business School, Taylor’s University, 47500 Subang Jaya, Malaysia
Ilan Alon: School of Business and Law, University of Agder, 4630 Kristiansand, Norway
Journal of Risk and Financial Management, 2019, vol. 12, issue 2, 1-15
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.
Keywords: ARIMA; artificial neural network; Bitcoin; cryptocurrency; static forecast (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:103-:d:241532
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