文章基本信息

标题：A Comprehensive Statistical Analysis of the Six Major Crypto-Currencies from August 2015 through June 2020
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作者：Vaz de Melo Mendes, Beatriz ; Fluminense Carneiro, André
期刊名称：Journal of Risk and Financial Management
印刷版ISSN：1911-8074
出版年度：2020
卷号：13
期号：9
页码：1-21
DOI：10.3390/jrfm13090192
语种：English
出版社：MDPI, Open Access Journal
摘要：After more than a decade of existence, crypto-currencies may now be considered animportant class of assets presenting some unique appealing characteristics but also sharing somefeatures with real financial assets. This paper provides a comprehensive statistical analysis of thesix most important crypto-currencies from the period 2015–2020. Using daily data we (1) showedthat the returns present many of the stylized facts often observed for stock assets, (2) modeled thereturns underlying distribution using a semi-parametric mixture model based on the extreme valuetheory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of longmemory as well as short memory in the GARCH volatility, (4) used an econometric approach tocompute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found thatthe crypto-coins’ price trajectories do not contain speculative bubbles and that they move togethermaintaining 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 basedbivariate dynamic measures of association. The analyses indicate that the strength of dependenceamong 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 foralternative diversified and profitable investments. To complete the analysis we provide a briefdiscussion on the effects of the COVID-19 pandemic on the crypto-market by including the firstsemester of 2020 data.
关键词：Bitcoin; crypto-currency; risk measures; pair-copulas; cointegrated VAR; EVT; COVID-19