Bitcoin return dynamics volatility and time series forecasting - Anand, Punit; Sharan, Anand Mohan (International Journal of Financial Studies, 2025)
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.
Number: 2
Keywords: ARMA; ARMA-GARCH; Econometrics; Economics; Financial economics; MAE; Mathematics; RMSE; Series (stratigraphy); Statistics; Time series; Volatility (finance); bitcoin; dept.fin; time series