Research Paper | Statistics | Nigeria | Volume 8 Issue 10, October 2019
Estimating and Forecasting Bitcoin Daily Returns Using ARIMA-GARCH Models
Emaeyak Xavier Udom
Abstract: This paper provides an evaluation of the forecasting performance of hybrid ARIMA-GARCH model in forecasting Bitcoin daily price returns. We combined ARIMA and GARCH model with Normal, Studentís t and Skewed studentís t distributions. To make the series stationary, Bitcoin daily price data was transformed to Bitcoin daily returns. By using Box-Jenkins method, the appropriate ARIMA model was obtained and for capturing volatilities of the returns series GARCH (1, 1) models with Normal, Studentís t and Skewed studentís t distributions was used. To evaluate the performance of the models, the study employs two measures, RMSE and MAE. The results reveal that ARIMA (2, 0, 1) -GARCH (1, 1) with Normal distribution outperform the other three in terms of out-of-sample forecast with minimum RMSE and MAE. The findings can aid investors, market practitioners, financial institutions, policymakers, and scholars.
Keywords: ARIMA, GARCH, Bitcoin returns, Hybrid ARIMA-GARCH
Edition: Volume 8 Issue 10, October 2019,
Pages: 376 - 382
How to Cite this Article?
Emaeyak Xavier Udom, "Estimating and Forecasting Bitcoin Daily Returns Using ARIMA-GARCH Models", International Journal of Science and Research (IJSR), Volume 8 Issue 10, October 2019, pp. 376-382, https://www.ijsr.net/get_abstract.php?paper_id=ART20201582
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