Next Article in Journal
Survey of Green Bond Pricing and Investment Performance
Next Article in Special Issue
Dynamic Connectedness between Bitcoin, Gold, and Crude Oil Volatilities and Returns
Previous Article in Journal
Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?
Previous Article in Special Issue
True versus Spurious Long Memory in Cryptocurrencies
Open AccessArticle

A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020

1
IM/COPPEAD (Institute of Mathematics/Instituto de Pós-Graduação e Pesquisa em Administração), Federal University at Rio de Janeiro, Rio de Janeiro 21941901, Brazil
2
COPPEAD (Instituto de Pós-Graduação e Pesquisa em Administração), Federal University at Rio de Janeiro, Rio de Janeiro 21941901, Brazil
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(9), 192; https://doi.org/10.3390/jrfm13090192
Received: 17 July 2020 / Revised: 17 August 2020 / Accepted: 19 August 2020 / Published: 25 August 2020
After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015–2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins’ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. View Full-Text
Keywords: Bitcoin; crypto-currency; risk measures; pair-copulas; cointegrated VAR; EVT; COVID-19 Bitcoin; crypto-currency; risk measures; pair-copulas; cointegrated VAR; EVT; COVID-19
Show Figures

Figure 1

MDPI and ACS Style

Vaz de Melo Mendes, B.; Fluminense Carneiro, A. A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020. J. Risk Financial Manag. 2020, 13, 192. https://doi.org/10.3390/jrfm13090192

AMA Style

Vaz de Melo Mendes B, Fluminense Carneiro A. A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020. Journal of Risk and Financial Management. 2020; 13(9):192. https://doi.org/10.3390/jrfm13090192

Chicago/Turabian Style

Vaz de Melo Mendes, Beatriz; Fluminense Carneiro, André. 2020. "A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020" J. Risk Financial Manag. 13, no. 9: 192. https://doi.org/10.3390/jrfm13090192

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop