Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models

Over the past years, cryptocurrencies have drawn substantial attention from media while attracting many investors. Since then, cryptocurrency prices experienced high fluctuations. In this paper, we forecast high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling to select best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which compare a multivariate DCC-GARCH(1,1) model intraday price volatility. evaluate results under MSE MAE loss functions. Statistical analyses demonstrate that univariate GJR-GARCH (1,1) shows superior predictive accuracy at all horizons, followed closely are models for process on out-of-sample data more accurately indicated asymmetric incidence shocks in market. The study determines evidence bidirectional shock transmission effects between pairs. Hence, DCC-GARCH can identify market’s cross-market transmissions. addition, introduce comparison using improvement rate (IR) metric comparing models. As result, different forecasting chosen benchmarking confirm trends model’s predictions.