Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model

Abstract

In recent years, cryptocurrencies have gained investor attention for their extreme volatility, but this has introduced financial risks that require accurate prediction models. Therefore, we propose the SHARV-MGJR model, which incorporates both ‘good-bad’ volatility, leverage effects, and current return information to enhance the accuracy of cryptocurrency market volatility predictions. Empirical results demonstrate that compared to GARCH-type models, the SHARV-MGJR model exhibits superior predictive accuracy in forecasting cryptocurrency market volatility. Furthermore, robustness tests confirm the superiority of the SHARV-MGJR model in predicting cryptocurrency market volatility.