How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?

Yaya, OlaOluwa S.; Ogbonna, Ahamuefula E.; Olubusoye, Olusanya E.

doi:10.1016/j.physa.2019.121732

Cited by 37 publications

(26 citation statements)

References 47 publications

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“…The adopted point for the sub-division was the peak point with respect to the bitcoin prices, being the most valuable cryptocurrency (Yaya et al, 2018…”

Section: Data and Empirical Resultsmentioning

confidence: 99%

“…Therefore, in addition to the full sample period considered, the series was further sub‐divided into different two sample periods—the pre‐crash and post‐crash periods. The adopted point for the sub‐division was the peak point with respect to the Bitcoin prices, being the most valuable cryptocurrency (Yaya et al, 2019). Consequently, the chosen break date was 17 December 2017, when the cryptocurrency (particularly, the Bitcoin) prices were observed to crash, thus, trending downwards 4 .…”

Section: Data and Empirical Resultsmentioning

confidence: 99%

“…The informational efficiency was observed in periods covering recent years. However, the various studies confirmed that the returns on cryptocurrencies are often times characterized by some salient features, which include unit roots, autocorrelation, non-linearity and long range dependence (see also Balcilar et al, 2017;Bouri et al, 2018 andYaya, et al, 2018;among others). As consistent with returns on asset prices, returns on cryptocurrencies also exhibit large volatility and strong persistence (see Cheah et al, 2018;Yaya, et al, 2018;among others).…”

Section: Introductionmentioning

confidence: 93%

“…However, the various studies confirmed that the returns on cryptocurrencies are often times characterized by some salient features, which include unit roots, autocorrelation, non-linearity and long range dependence (see also Balcilar et al, 2017;Bouri et al, 2018 andYaya, et al, 2018;among others). As consistent with returns on asset prices, returns on cryptocurrencies also exhibit large volatility and strong persistence (see Cheah et al, 2018;Yaya, et al, 2018;among others). In contrast to the stance of the proponents of the characteristics of market efficiency of cryptocurrencies, some other studies, (Cheah et al, 2018; among others) find nonhomogeneous informational inefficiency, as well as cointegrating relationships among the markets investigated.…”

Section: Introductionmentioning

confidence: 93%

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Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration

et al. 2020

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This paper investigates both market efficiency and volatility persistence in 12 cryptocurrencies during pre-crash and post-crash periods. We were motivated by the erroneous belief of some authors that driving currency, Bitcoin is inefficient. By considering robust fractional integration methods in linear and nonlinear set up, we found that markets of Bitcoin and most altcoins considered in our samples can be dubbed as efficient, and these are highly volatile particularly in the post-crash sample that we are now. These volatilities will then persist for shorter period than in the pre-crash period. Our work therefore renders important information to cryptocurrency market participants and portfolio managers.

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“…The adopted point for the sub-division was the peak point with respect to the bitcoin prices, being the most valuable cryptocurrency (Yaya et al, 2018…”

Section: Data and Empirical Resultsmentioning

confidence: 99%

Section: Data and Empirical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 93%

See 2 more Smart Citations

Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration

et al. 2020

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“…While the cryptocurrency market collapsed in 2018the total market capitalization fell down from 800 to 100 billion USD due to rumors about possible bans from several countries due to lacks of regulations and the increasing number of frauds (Bloomberg, 2018;Williams-Grut, 2018;Yaya et al, 2019) it re-surged in the first half of 2019 to then plummet and rise again. Given the global interest in cryptocurrencies as investment opportunities, the advancement of our limited academic understanding of the drivers of ICOs' success could not be timelier, especially because the realization of blockchains' potential necessarily depends on the reliability of cryptocurrencies as a mean to fund new entrepreneurial ventures.…”

Section: Introductionmentioning

confidence: 99%

Collecting money through blockchain technologies: first insights on the determinants of the return on Initial Coin Offerings

Cappa

Pinelli

2020

Information Technology for Development

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Initial Coin Offerings (ICOs), i.e. the initial offer of a crypto-token, represent an increasingly popular method to raise money. However, the determinants of ICOs' returns for investors are still overlooked. Following this cue, the empirical outcomes of our study based on crypto-tokens issued between 2017 and 2018 evidence the main determinants of ICOs' returns: first, crypto-tokens returns are positively associated to Ether's returns; second, ICO price is negatively associated to later price increases; third, crypto-tokens returns are lower when they are offered in presale; finally, the more a crypto-token price increases between the ICO and the listing-date on the secondary-market and the more its price rises in the following month. In so doing, we contribute to a better understanding of the ICO phenomenon and highlight which aspects may benefit fund raising, which are relevant for the establishment of new startups and more broadly for the economic development of a country.

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Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

106

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?

Cited by 37 publications

References 47 publications

Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration

Market efficiency and volatility persistence of cryptocurrency during pre‐ and post‐crash periods of Bitcoin: Evidence based on fractional integration

Collecting money through blockchain technologies: first insights on the determinants of the return on Initial Coin Offerings

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

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