2021
DOI: 10.1371/journal.pone.0246209
| View full text |Cite
|
Sign up to set email alerts
|

Abstract: This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial c… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

2
4
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 13 publications
(6 citation statements)
references
References 87 publications
2
4
0
Order By: Relevance
“…Surprisingly, in the case of unfavorable news arrival, the graph indicates a persistent overreaction pattern of investors, yet featured by a more tranquil trend than in the previously studied periods. In line with [ 38 , 43 ], our study holds that there are signs of enhanced efficiency of Bitcoin over time.…”
Section: Resultssupporting
confidence: 87%
See 1 more Smart Citation
“…Surprisingly, in the case of unfavorable news arrival, the graph indicates a persistent overreaction pattern of investors, yet featured by a more tranquil trend than in the previously studied periods. In line with [ 38 , 43 ], our study holds that there are signs of enhanced efficiency of Bitcoin over time.…”
Section: Resultssupporting
confidence: 87%
“…In contrast, [ 39 ] validated the weak efficient hypothesis within the same timespan as the latter study, reasoning that everything is not so negative about Bitcoin (see also [ 40 ]). Additional evidence for the trend towards informational efficiency of this digital currency in recent times comes from [ 7 , 41 43 ]. In this same context, [ 44 ] highlighted several periods with significant anti-persistent memory in the BTC-USD series.…”
Section: Introductionmentioning
confidence: 99%
“…We obtained the following findings: firstly, although the distributions of these cryptocurrency time series have a bell curve shape at all timescales considered, they are not (from the Q-Q plot and Lilliefors test) normally distributed; secondly, however, there appears to be evidence to say that Aggregational Gaussianity exists in all cryptocurrencies used in this present study from the Q-Q plots. This result is in line with existing findings in the cryptocurrency market such as [ 54 , 55 ].…”
Section: Data Descriptionsupporting
confidence: 93%
“…On CN Links: [207], Epidemics: [208,209], Message, memes: [210,211] Of CN Links: [212,213,214], Nodes: [215], Hubs: [216], Communities: [217], Person's social signature: [218], Patterns: [219,220], Processes Rumors: [221], Epidemics: [222], Emotions: [223] Economics and Market Analysis Inflation [224,225,226,227,228,226,229,227,230,231,228] Expected Returns [232,233,234,233,235,236] Exchange Rates and Crypto-currencies [237,238,239,240,241,242,243,244,245,246,246,247] Stock Market [15,248,…”
Section: Complex Networkmentioning
confidence: 99%
“…The results obtained in this case showed that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidences of persistence. In [240] a complete analysis of volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market is carried out. Among other efficiency-related measures, the Hurst exponent, multifractal degree, and kurtosis are obtained for the Bitcoin price, return and volatility time series.…”
Section: Persistence In Economics and Market Analysismentioning
confidence: 99%