Long- and short-term cryptocurrency volatility components: a GARCH-MIDAS analysis
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 realiz...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
10 May 2018
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| In: |
Journal of risk and financial management
Year: 2018, Volume: 11, Issue: 2 |
| ISSN: | 1911-8074 |
| DOI: | 10.3390/jrfm11020023 |
| Subjects: | |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.3390/jrfm11020023 Verlag, kostenfrei, Volltext: http://www.mdpi.com/1911-8074/11/2/23/pdf Resolving-System, kostenfrei: http://hdl.handle.net/10419/238870 Verlag, Terms of use, 46: http://creativecommons.org/licenses/by/4.0/ 
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| Author Notes: | Christian Conrad, Anessa Custovic and Eric Ghysels |
MARC
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| 520 | |a 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. | ||
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