A systematic review of the bubble dynamics of cryptocurrency prices

Kyriazis, Nicholas; Papadamou, Stephanos; Corbet, Shaen

doi:10.1016/j.ribaf.2020.101254

Cited by 123 publications

(53 citation statements)

References 104 publications

(99 reference statements)

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“…(2019) has provided an integrated overview of cryptocurrency characteristics. Moroever, there is a survey paper on the bubble characteristics of cryptocurrencies ( Kyriazis et al., 2020 ) and reviews on the efficiency of cryptocurrency markets ( Kyriazis, 2019 ) and the nexus of Bitcoin with gold ( Kyriazis, 2020 ). Our overview and empirical testing adds to relevant literature by casting light on a specific aspect of behaviour in digital currency markets.…”

Section: Introductionmentioning

confidence: 99%

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.

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Section: Introductionmentioning

confidence: 99%

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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“…Car manufacturers aiming to achieve not only applications of blockchain would create new business model, but there is a propensity et al ( 2020), which explored the potential innovation of blockchain technology has shown that business venture proposals (Delafenestre, 2019) factor that can affect the adoption of blockchain in achieving operational excellence in this Therefore, exploring government regulatory and legal issue challenges as an environmental Though the use of cryptocurrencies in some countries is unrestricted (Chang, et al, 2020a). cryptocurrencies such as Bitcoin as unconstitutional or illegal coin (Kyriazis et al 2020). systems such as cryptocurrencies.…”

Section: New Business Model Opportunitiesmentioning

confidence: 99%

A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive industry

Upadhyay

Ayodele

Garza‐Reyes³

2020

JGOSS

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Purpose This paper aims to explore the challenges and opportunities of blockchain technology adoption from the lens of the technological–organisational–environmental (TOE) framework for operational excellence in the UK automotive industry context. Design/methodology/approach The research methodology of this study follows a systematic review approach, which analyses existing academic published research papers in the top 35 academic journals. There was no specific timeframe established for this study and shortlisting the articles through a set of used keywords. A sample of 71 articles was shortlisted and analysed to provide a discussion on technological and management challenges and opportunities of blockchain adoption from the lens of the TOE framework for operational excellence. Findings The findings of this study present significant theoretical and managerial implications and deep understanding for firms seeking to understand the challenges and opportunities of blockchain adoption for their operational excellence. Research limitations/implications The systematic literature approach was considered for the present study to explore existing academic papers on technological and management challenges and opportunities from the lens of TOE framework for operational excellence, whereas a more specified method meta-analysis can be considered for future research. The study has been explored in the UK automotive industry context, which has been considered as the limitation of generalisation across countries and industries. Originality/value This paper represents the most comprehensive literature study related to the technological and management challenges and opportunities of blockchain from the TOE framework angle for operational excellence.

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“…To provide assurances and clarity with regards to broad financial stability, the abovementioned crash risks have motivated many researchers to understand the drivers behind Bitcoin crashes and push further investigation on the existence of their early warning signals (EWSs). By doing so, many researches have applied bubble detection to detect speculative bubbles, which believed will burst as crashes in Bitcoin, see [52] and references therein. [53] shows that the adaptive multilevel time series detection methodology based on the Log Periodic Power Law Singularity model is better than the conventional bubble detection methods to diagnose bubbles and forecast crashes in the cryptocurrency market, highlighting the importance of high-dimensional approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

2020

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This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their 1-norms. Consequently, a new representative is attained, called 1-norms time series. The 1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall's tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare 1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the 1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the 1norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin. INDEX TERMS Cryptocurrencies, bitcoin, persistent homology, critical transition, early warning signals.

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A systematic review of the bubble dynamics of cryptocurrency prices

Cited by 123 publications

References 104 publications

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive industry

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

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