Abstract
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.
| Original language | English |
|---|---|
| Article number | 103 |
| Journal | Journal of Risk and Financial Management |
| Volume | 12 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2019 |
| Externally published | Yes |
Keywords
- ARIMA
- Bitcoin
- artificial neural network
- cryptocurrency
- static forecast
All Science Journal Classification (ASJC) codes
- Accounting
- Business, Management and Accounting (miscellaneous)
- Finance
- Economics and Econometrics