Forecasting Ethereum STORJ Token Prices: Comparative Analyses of Applied Bitcoin Models
Rhonda Bush, Soo-Hyun Choi · 2019
The research on forecasting Ethereum STORJ token has not been widely studied compared to forecasting Bitcoin. The objective of this paper is threefold: apply existing Bitcoin price forecasting models to the Ethereum STORJ token price; evaluate the dynamics of the model predictive utility across three time horizons (h=5 days, h=20 days and h=50 days); and determine if Ethereum STORJ token clustering coefficients impact the effectiveness of the forecasting model. We choose Bitcoin forecasting models of: ARIMA, ARMA-GARCH, VAR, alpha-Sutte Indicator and NNAR. Model effectiveness is analyzed using RMSE, MAE and MAPE. We find that VAR outperforms all models in the short and mid-term horizons (h=5 and h=20 days) and NNAR outperforms all models in the long-term horizon (h=50 days). Non-linearity, the intrinsic value that Neural Network has, may strongly effect the forecast accuracy result. When adding the clustering coefficient to ARIMA, we find that the variable is significant but only marginally improves the forecasting of the Ethereum STORJ token. The VAR model, which includes the clustering coefficient is shown to better forecast Ethereum STORJ prices in the short and mid-term forecasting horizons. The NNAR model is a better model for the long-term forecasting horizon.