Artificial Neural Networks for Realized Volatility Prediction in Cryptocurrency Time Series

Abstract

Realized volatility (RV) is defined as the sum of the squares of logarithmic returns on high-frequency sampling grid and aggregated over a certain time interval, typically a trading day in finance. It is not a priori clear what the aggregation period should be in case of continuously traded cryptocurrencies at online exchanges. In this work, we aggregate RV values using minute-sampled Bitcoin returns over 3-h intervals. Next, using the RV time series, we predict the future values based on the past samples using a plethora of machine learning methods, ANN (MLP, GRU, LSTM), SVM, and Ridge Regression, which are compared to the Heterogeneous Auto-Regressive Realized Volatility (HARRV) model with optimized lag parameters. It is shown that Ridge Regression performs the best, which supports the auto-regressive dynamics postulated by HARRV model. Mean Squared Error values by the neural-network based methods closely follow, whereas the SVM shows the worst performance. The present benchmarks can be used for dynamic risk hedging in algorithmic trading at cryptocurrency markets.