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Article

Multivariate Analysis of Cryptocurrencies

MEMOTEF Department, Sapienza University of Rome, 00185 Rome, Italy
Academic Editors: Fredj Jawadi and Marc S. Paolella
Econometrics 2021, 9(3), 28; https://doi.org/10.3390/econometrics9030028
Received: 17 March 2021 / Revised: 24 June 2021 / Accepted: 28 June 2021 / Published: 1 July 2021
Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework. View Full-Text
Keywords: cryptocurrency; covariance matrix; Dynamic Conditional Correlation; Double Asymmetric GARCH–MIDAS cryptocurrency; covariance matrix; Dynamic Conditional Correlation; Double Asymmetric GARCH–MIDAS
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MDPI and ACS Style

Candila, V. Multivariate Analysis of Cryptocurrencies. Econometrics 2021, 9, 28. https://doi.org/10.3390/econometrics9030028

AMA Style

Candila V. Multivariate Analysis of Cryptocurrencies. Econometrics. 2021; 9(3):28. https://doi.org/10.3390/econometrics9030028

Chicago/Turabian Style

Candila, Vincenzo. 2021. "Multivariate Analysis of Cryptocurrencies" Econometrics 9, no. 3: 28. https://doi.org/10.3390/econometrics9030028

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