Kimchi premium and speculative trading in bitcoin

Eom, Yunsung

doi:10.1016/j.frl.2020.101505

Cited by 17 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Findings reveal that the Producer Price Index exerts significant effects on Bitcoin prices only one day before the FOMC announcement while no significant impacts from macroeconomic announcements are found in general. Eom [2020] by using Bitcoin data from Korea and the US and employing Generalised Method of Moments (GMM) estimations support that the high trading volume and price instability can explain the Kimchi premium. Higher Bitcoin bubbles lead to a clearer nexus between trading volume and premium.…”

Section: Literature On Cryptocurrency Bubble Price Formationmentioning

confidence: 97%

A systematic review of the bubble dynamics of cryptocurrency prices

Kyriazis

Papadamou

Corbet

2020

Research in International Business and Finance

123

View full text Add to dashboard Cite

Section: Literature On Cryptocurrency Bubble Price Formationmentioning

confidence: 97%

A systematic review of the bubble dynamics of cryptocurrency prices

Kyriazis

Papadamou

Corbet

2020

Research in International Business and Finance

123

View full text Add to dashboard Cite

“…The second feature found in BTC series, long memory on volatility, could be related to speculative bubbles (Geuder et al , 2019 Li et al ,2021 and Eom, 2021) and extreme values (Kayal and Balasubramanian, 2021 and Hussain et al , 2021) as an inherent behavior exhibited by BTC returns. In other words, long memory on volatility implies that after occurs financial disequilibria, risk/volatility tends to increase not just immediately, but in the long run.…”

Section: Discussionmentioning

confidence: 99%

Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic

Castro

Ortiz

Cabello-Rosales

2022

SEF

View full text Add to dashboard Cite

Purpose The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility. Design/methodology/approach The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021). Findings Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC. Originality/value Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.

show abstract

“…Indeed, at the end of 2017, the price of Bitcoin (as well as the ones of the other cryptocurrencies, like Ethereum or Litecoin) increased by almost 600% (reaching an all-time high value of US$19,475.80) before falling by 80% in few weeks, until January 2018 [3,[20][21][22]. This is the biggest bubble in the cryptocurrency's history so far.…”

Section: Related Literaturementioning

confidence: 99%

A Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble

Bonifazi

Corradini

Ursino

et al. 2021

Journal of Information Science

View full text Add to dashboard Cite

In this article, we present a Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behaviour of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneuvered behind the Ethereum bubble analysed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.

show abstract

Kimchi premium and speculative trading in bitcoin

Cited by 17 publications

References 20 publications

A systematic review of the bubble dynamics of cryptocurrency prices

A systematic review of the bubble dynamics of cryptocurrency prices

Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic

A Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble

Contact Info

Product

Resources

About