Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis

By Christian Conrad, A. Custovic, Eric Ghysels. 2018

265 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/2acf7458d9a3b3c4b1186ddc05bc0e4374cccb6c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Elie Bouri
2. T. Walther
3. Tony Klein
4. David Martínez-Rego
5. Carmine Ventre
6. Leslie Kanthan
7. Lingbo Li
8. Fan Wu

In Information Systems

1. T. Walther
2. Tony Klein
3. David Martínez-Rego
4. Carmine Ventre
5. Leslie Kanthan
6. Lingbo Li
7. Fan Wu
8. Fan Fang

Citations in the corpus

• Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “…models see, for example, Conrad, Loch, and Rittler (2014), Opschoor, van Dijk, and van der Wel (2014), Dominicy and Vander Elst (2015), Lindblad (2017), Amendola, Candila, and Scognamillo (2017), Pan, Wang, Wu, and Yin (2017), Conrad, Custovic, and Ghysels (2018), and Borup and Jakobsen (2019).”
• Testing for Bubbles in Cryptocurrencies with Time-Varying Volatility (2018, unknown): “For example, Gronwald (2014) uses an autoregressive jump-intensity GARCH model to describe Bitcoin return dynamics until 2014, Scaillet et al (2017) analyze the jump behavior of Bitcoin transaction data from 2011 to 2013, and Conrad et al. (2018) decompose volatility into long- and short-term compon”
• Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance (2018, unknown): “Conrad et al. (2018) examine the drivers of long-term volatility of Bitcoin and compare them to other assets class, e.g. Gold.”
• Cryptocurrency trading: a comprehensive survey (2020, methodology): “[113] used the GARCHMIDAS model to extract long and short-term volatility components of the Bitcoin market.”
• Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, background): “(2019) provide evidence that global economic policy uncertainty enhances the hedging effectiveness of Bitcoin and the prediction of Bitcoin volatility, while Conrad et al. (2018) explore the factors driving Bitcoin’s volatility and provide evidence that Bitcoin volatility is closely linked to global”