Forecasting Ethereum Volatility Using Deep Learning and Time Series Models

Abstract

The aim of this paper is to forecast the volatility of Ethereum for a specified time period. To achieve this, we evaluated and compared the performance of five different models: Generalised Autoregressive Conditional Heteroscedasticity (GARCH), Long Short-Term Memory (LSTM), Random Walk Model (RWM), Neural Network Baseline Metrics - Fully Connected Network, and Baseline Model. After applying these models, we compared the estimated volatilities with the realized volatilities to assess the prediction accuracy. Considering the result comprehensively, the GARCH(1,1) is the most accurate model among these five. Our results revealed interesting insights into the nature of Ethereum, which behaves differently from traditional currencies. However, given Ethereum's early-stage behavior, future results may vary.