Abstract
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.
Highlights
Bitcoin is surely not short on publicity as its rise, subsequent decline and volatile swings have drawn the attention from academics and business leaders alike
We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility
The GARCH-MIxed Data Sampling (MIDAS) model allows us to investigate whether US stock market volatility has an effect on long-term Bitcoin volatility
Summary
Bitcoin is surely not short on publicity as its rise, subsequent decline and volatile swings have drawn the attention from academics and business leaders alike. We use the GARCH-MIDAS model of Engle et al (2013) for investigating the economic determinants of long-term Bitcoin volatility. While all the previous studies considered Bitcoin returns/volatility as well as their potential determinants at the same (daily) frequency, the MIxed Data Sampling (MIDAS) technique offers a unique framework to investigate macroeconomic and financial variables that are sampled at a lower (monthly) frequency than the Bitcoin returns as potential drivers of Bitcoin volatility. While most previous studies focused on short-term relationships using exclusively daily data, our results highlight the importance of investigating the relationship between long-term Bitcoin volatility and its economic drivers. The long-term component is expressed as a function of observable explanatory variables This allows us to investigate the financial and macroeconomic determinants of Bitcoin volatility. Data are collected from a number of sources and are described in more detail in what follows
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