We use the GARCH-MIDAS model to extract the long-and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.

We investigate the relationship between long-term U.S. stock market risks and the macroeconomic environment using a two component GARCH-MIDAS model.Our results provide strong evidence in favor of counter-cyclical behavior of longterm stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for stock market volatility. While the term spread and housing starts are leading variables with respect to stock market volatility, for corporate profits and the unemployment rate expectations data from the Survey of Professional Forecasters regarding the future development are most informative.Our results suggest that macro variables carry information on stock market risk beyond that contained in lagged realized volatilities, in particular when it comes to long-term forecasting.