Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?

Bouri, Elie; Jalkh, Naji; Molnár, Péter; Roubaud, David

doi:10.1080/00036846.2017.1299102

Cited by 175 publications

(214 citation statements)

References 19 publications

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“…Limited connectedness to volatility in other markets makes Bitcoin a candidate to be an efficient diversifier. Such potential is in accordance with Bouri, Jalkh, Molnár, and Roubaud (2017), whose results support role of Bitcoin as a diversifier, for certain periods of time even as a hedge or safeheaven to commodities. According to our results, the diversifying feature of Bitcoin could be applied to a broader class of assets.…”

Section: Overall Connectednesssupporting

confidence: 85%

“…Rapid development of cryptocurrencies attracted lot of attention in recent research (Brandvold, Molnár, Vagstad, and Valstad, 2005;Bouri, Jalkh, Molnár, and Roubaud, 2017;Urquhart, 2016;Katsiampa, 2017;Urquhart, 2017;Nadarajah and Chu, 2017). Although the role of cryptocurrencies can still be disputed, they are certainly subject of rising awareness, and must be taken into account as a valid economic phenomenon.…”

Section: Introductionmentioning

confidence: 99%

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Do cryptocurrencies and traditional asset classes influence each other?

Kurka

2019

Finance Research Letters

182

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract:Large stream of literature studies interconnectedness among various assets that are relevant in current global markets. Transmission of shocks between cryptocurrencies and traditional asset classes is, however, not understood at all, but should not be ignored due to increasing influence of cryptocurrencies in recent years. In this paper, we study how shocks between the most liquid representatives of the traditional asset classes including commodities, foreign exchange, stocks, financials, and cryptocurrencies are being transmitted. Generally, we document very low level of connectedness between the main cryptocurrency and other studied assets. The only exception is gold which receives substantial amount of shocks from cryptocurrency market. Our findings are important since we show that cryptocurrencies play role in global markets, and the results could also be useful in portfolio diversification schemes. Moreover, we find significant positive asymmetry in spillovers between the studied assets, which is in contradiction to previous studies conducted on assets from a single asset class.

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Section: Overall Connectednesssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Do cryptocurrencies and traditional asset classes influence each other?

Kurka

2019

Finance Research Letters

182

View full text Add to dashboard Cite

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“…They found that market forces and Bitcoin attractiveness are two major factors in determining the Bitcoin price. Bouri et al (2017) studied the relationship between Bitcoin and commodities, focusing on the energy market. Their results showed that Bitcoin could work as a diversifier in this market.…”

Section: Bitcoinmentioning

confidence: 99%

Predicting changes in Bitcoin price using grey system theory

Jalali

Heidari

2020

Financ Innov

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Bitcoin is currently the leading global provider of cryptocurrency. Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage. In recent years, the Bitcoin network has attracted investors, businesses, and corporations while facilitating services and product deals. Moreover, Bitcoin has made itself the dominant source of decentralized cryptocurrency. While considerable research has been done concerning Bitcoin network analysis, limited research has been conducted on predicting the Bitcoin price. The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory. The first order grey model (GM (1,1)) is used for this purpose. It uses a firstorder differential equation to model the trend of time series. The results show that the GM (1,1) model predicts Bitcoin's price accurately and that one can earn a maximum profit confidence level of approximately 98% by choosing the appropriate time frame and by managing investment assets.

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“…Given that Bitcoin does not provide any cash flows like most of equities and bonds, its price is often affected by supply-demand imbalances, Bitcoin's attractiveness Kristoufek, 2015), macrofinancial developments (Li & Wang, 2017), technological factors and computer programming enthusiasts (Yelowitz & Wilson, 2015), energy (Bouri, Jalkh, Molnár, & Roubaud, 2017), or trading volume (Balcilar, Bouri, Gupta, & Roubaud, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

et al. 2018

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Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.

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Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven?

Cited by 175 publications

References 19 publications

Do cryptocurrencies and traditional asset classes influence each other?

Do cryptocurrencies and traditional asset classes influence each other?

Predicting changes in Bitcoin price using grey system theory

Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

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