Forecasting Volatility of Ether- An empirical evaluation of volatility models and their capacity to forecast one-day-ahead volatility of Ether

Abstract

This study evaluates the performance of volatility models in forecasting one-day-ahead volatility of the cryptocurrency Ether. The selected models are: GARCH, EGARCH, GJR-GARCH, SMA9, SMA20, and EWMA. We investigate both in-sample performance and out-of-sample performance. In-sample performance concerns only the set of GARCH models, where the parameters of the models are estimated and the degree of goodness-of-fit is evaluated using Akaike Information Criterion and Bayesian Information Criterion. For out-of-sample performance, we use Realized Volatility as a measure of ex-post volatility. The models are evaluated by conducting the Diebold- Mariano test for statistical difference between the models, and two loss functions: mean squared errors (MSE) and mean absolute errors (MAE). The results from the in-sample performance show that GARCH minimizes AIC and BIC using Student’s tdistribution as well as BIC using the Gaussian distribution. The best model in terms of AIC using the Gaussian distribution was found to be GJR-GARCH. The out-ofsample results show that EGARCH is the best performing model using MSE, while SMA9 is the optimal model using MAE. However, the models are not statistically different and either one may be considered for forecasting purposes.