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, Student’s t and Skewed student’s 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, Student’s t and Skewed student’s 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

How to Cite?: Emaeyak Xavier Udom, "Estimating and Forecasting Bitcoin Daily Returns Using ARIMA-GARCH Models", Volume 8 Issue 10, October 2019, International Journal of Science and Research (IJSR), Pages: 376-382, https://www.ijsr.net/getabstract.php?paperid=ART20201582, DOI: https://dx.doi.org/10.21275/ART20201582

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