Portfolio diversification with virtual currency: Evidence from bitcoin

Guesmi, Khaled; Saadi, Samir; Abid, Ilyes; Ftiti, Zied

doi:10.1016/j.irfa.2018.03.004

Cited by 506 publications

(336 citation statements)

References 26 publications

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“…These authors recommend that bitcoin should be treated as a speculative asset rather than a credit currency. Guesmi et al (2018) use GARCH models to study the usefulness of using Bitcoin to hedge investments in gold, oil and emerging market stocks. All portfolios are two-asset portfolios that include Bitcoin and one other asset.…”

Section: What Is Bitcoin-currency or Asset?mentioning

confidence: 99%

Can Bitcoin Replace Gold in an Investment Portfolio?

Henriques

Sadorsky

2018

JRFM

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Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.

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Section: What Is Bitcoin-currency or Asset?mentioning

confidence: 99%

Can Bitcoin Replace Gold in an Investment Portfolio?

Henriques

Sadorsky

2018

JRFM

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“…With crypto-currencies being integrated with the traditional financial assets (see, Tony et al 2018;Khaled et al 2018 andHenriques andSadorsky 2018), there is bound to be more investor attention and the possibility of market being more liquid. In such a scenario, exploring the potential for diversification in crypto-currency markets is of paramount importance.…”

Section: Introductionmentioning

confidence: 99%

Co-movement in crypto-currency markets: evidences from wavelet analysis

Kumar

Ajaz

2019

Financ Innov

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We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given cryptocurrency prices are estimated across different timescales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.

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“…In the absence of underlying assets in the cryptocurrency market, it is difficult to establish market efficiency without carrying out an adequate analysis of the market behaviour. Efficiency in the cryptocurrency market cannot simply be assessed by observing the level of correlation between cryptocurrencies and other speculative assets or economic variables, as shown by a number of scholars (See, e.g., Chuen et al, 2017;Guesmi et al, 2019). Instead, additional test procedures must be employed to understand what factually drives efficiency of cryptocurrency.…”

Section: Literature Reviewmentioning

confidence: 99%

Herd behaviour in the cryptocurrency market: Fundamental vs. spurious herding

Senarathne¹,

Jiang²

2020

EurJ Appl Econ

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This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang et al. (2000) and Galariotis et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute deviation of return can only be explained by GSCI oil and gold index return, but no relationship exists between cross-sectional absolute deviation of return and other regression variables, such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that a strong tendency exists to herd on non-fundamental information that explains cross-sectional absolute deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during an upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in more efficient outcomes, the above findings do not encourage the diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo-efficient trading platform for speculative investors. Implications for future research are discussed.

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Portfolio diversification with virtual currency: Evidence from bitcoin

Cited by 506 publications

References 26 publications

Can Bitcoin Replace Gold in an Investment Portfolio?

Can Bitcoin Replace Gold in an Investment Portfolio?

Co-movement in crypto-currency markets: evidences from wavelet analysis

Herd behaviour in the cryptocurrency market: Fundamental vs. spurious herding

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