Time-varying properties of asymmetric volatility and multifractality in Bitcoin

Takaishi, Tetsuya

doi:10.1371/journal.pone.0246209

Cited by 14 publications

(10 citation statements)

References 87 publications

(172 reference statements)

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“…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%

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An analysis of investors’ behavior in Bitcoin market

2022

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As an emerging digital asset, Bitcoin has been traded for more than a decade, reaching an impressively high market capitalization and continuing to expand its volume of trading at a rapid pace. Many countries have legalized or are considering legalizing a trading platform for this asset, and a set of companies worldwide accept it as a medium of exchange. As a result of this expansion, many studies in finance literature have focused on studying the efficiency of this cryptocurrency. In line with this literature, this paper investigates, using the abnormal returns and abnormal trading volumes methodologies, the dynamics of investors’ reaction to the arrival of unexpected favorable and unfavorable information regarding the Bitcoin market in the context of the three famous hypotheses: the overreaction, the uncertain information, and the efficient market hypotheses. Overall, we find evidence confirming that the Bitcoin market tends to mature over time. More precisely, over the entire analyzed period, investors behave in accordance with the predictions of the uncertain information hypothesis when positive and negative events occur. However, splitting the timespan into sub-periods provides interesting insights. Remarkably in this respect is the fact that starting with the second sub-period, the response of investors in the Bitcoin market supports, in a moderate manner, the postulate of the efficient market hypothesis when favorable events are addressed. Moreover, our findings reveal that during the pandemic period, the efficiency of Bitcoin has increased, thus turning this stressful period into an advantage for this cryptocurrency. This improved market efficiency is also supported by the abnormal trading volume analysis.

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Section: Resultssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

An analysis of investors’ behavior in Bitcoin market

2022

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“…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%

The Cryptocurrency Market in Transition before and after COVID-19: An Opportunity for Investors?

Nguyen

Tan

Bezbradica

et al. 2022

Entropy

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We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

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“…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%