Can U.S. macroeconomic indicators forecast cryptocurrency volatility?

Abstract

This research examines the ability of 28 U.S. macroeconomic variables to forecast the volatility of six cryptocurrencies. In- and out-of-sample analyses are performed to validate their forecasting ability. Our analysis shows that during the full-sample period, 15 variables display forecasting ability, while post-COVID-19 period, this number is 17. Among these variables, the most influential include the consumer confidence index, leading economic index, consumer price index, U.S. exports and U.S. imports. Importantly, the predictive ability of these variables appears to have strengthened during the post-COVID-19 period. The out-of-sample results confirm the effectiveness of those macroeconomic variables in the in-sample tests. Furthermore, the robustness test reveals that incorporating these U.S. macroeconomic variables can enhance the performance of the GARCH volatility model. In this study, combination methods are used to enhance forecasting stability and are proven to have good forecasting ability. Our research also indicates that integrating global macroeconomic variables can enhance forecasting ability while recognizing the valuable information provided by U.S. macroeconomic variables. Additionally, we find that variables such as the short-term government bond yield and the M1 money supply emerge as important predictors of cryptocurrency bubbles.