This study employs the ADCC-GARCH approach to investigate the dynamic correlation between bitcoin and 14 major financial assets in different time-frequency dimensions over the period 2013-2021, for which the risk diversification, hedging and safe-haven properties of bitcoin for those traditional assets are further examined. The results show that, first, bitcoin is positively linked to risk assets, including stock, bond and commodity, and negatively linked to the U.S. dollar, which is a safe-haven asset, so bitcoin is closer in nature to a risk asset than a safe-haven asset. Second, the high short-term volatility and speculative nature of the bitcoin market makes its long-term correlation with other assets stronger than the short-term. Third, the positive linkage between the prices of bitcoin and risk assets increases sharply under extreme shocks (e.g., the outbreak of COVID-19 in early 2020). Fourth, bitcoin can hedge against the U.S. dollar, and in the long term, bitcoin can hedge against the Chinese stock market and act as a safe haven for the U.S. stock market and crude oil. However, for most other traditional assets, bitcoin is only an effective diversifier.
Bassam A. Ibrahim, Ahmed A. Elamer, Hussein A. Abdou
This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.
This paper aims to analyze the volatility spillover relationship between cryptocurrencies and stablecoins dynamically. Within the scope of the study, the daily closing price data of Bitcoin (BTC), Ethereum (ETH), BNB cryptocurrencies, and Tether (USDT) and USD Coin (USDC) stablecoins covering the period from January 1, 2019 to April 6, 2022 was analyzed using the Q-VAR model. Our results suggest that the volatility spillover between the cryptocurrency and stablecoins increased during the Covid-19 pandemic. Moreover, the direction and severity of volatility spillover between cryptocurrencies and stablecoins are affected by global events. While the relationship between cryptocurrencies and stablecoins themselves is strong, the relationship between each other is weak. Our findings suggest that global events influence the interaction between crypto-assets and that cryptocurrencies and stablecoins can be good diversifiers for each other. These findings have important implications for financial market regulators, portfolio investors, and academic research.
Given the broad scope of Ethereum and the wide range of its decentralized applications, this paper investigates its hedging and safe haven capabilities against main fiat currencies, stock and bond indices in the US and Europe, and crude oil and gold markets. We use daily data from January 2016 until February 2021 and apply percentile regressions and crisis event interaction analysis by selecting four worldwide events including US presidential elections, the Brexit referendum, and COVID-19. We reveal that Ethereum does not act as a hedge or a safe haven against fiat currencies, stock and bond indices, and gold. However, it does act as a strong safe haven against crude oil in calm and turbulent periods and against European bonds during market turbulence. The study provides insights to regulators and investors into the potential role of Ethereum in investment decision-making and protecting financial market participants in the US and EU.
Purpose The authors attempt to explore fat tails and network interlinkages of oil prices and the six largest cryptocurrencies from 1st January 2018 and 1st August 2021. The authors also investigate the influences of the COVID-19 pandemic on these network interlinkages. Design/methodology/approach The authors follow Diebold and Yilmaz (2012) to calculate the spillover index the dynamic correlation coefficient model firstly employed by Engle (2002) to study how the volatility of oil prices are transmitted to those of cryptocurrency return and liquidity and vice versa. Findings The results confirm the presence of time-varying interlinkages between the volatilities of the oil market and the cryptocurrency market. Notably, uncertain events like the COVID-19 health crisis significantly influence the time-varying interlinkages they augment dramatically during the COVID-19 health crisis. The turbulence of the cryptocurrency market, especially from Bitcoin and Ethereum, significantly impacts those of the oil market. The role of the oil market in transmitting the effect of respective shocks to the cryptocurrency market, on the other hand, is time-varying, which is only reported when the COVID-19 pandemic first appeared at the beginning of 2020. The turbulence of the cryptocurrency market in the system is greatly explained by themself rather than a transmission mechanism of shocks to the oil market. Practical implications Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets. Originality/value The most significant benefit of the approach is how simple it is to calculate net pairwise connectivity, which identifies transmission channels between these commodity and financial markets. The authors are also the first to use the quasi-maximum likelihood (QML) estimator to estimate the DCC model to measure the volatility spillover index to reflect the level of interdependence between the different markets. By using a daily and up to date database, the authors can observe the role of each market in transmitting and receiving the shocks between two different sub-periods: (1) before and (2) during the COVID-19 pandemic crisis.
This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan. It uses monthly data from 31 July 2010 to 30 August 2020 and employs the wavelet coherence method, Toda-Yamamoto, and nonlinear Granger-causality tests. The empirical results show that (i) Bitcoin prices influence M2 and interact with inflation and EPU. In the short term, inflation affects Bitcoin price positively, supporting Bitcoin as an inflation hedged instrument in Japan. Both in Japan and the U.K., the short-term effects of M2 on Bitcoin prices are negative, while EPU's effects on Bitcoin prices are positive, (ii) a bidirectional Toda-Yamamoto Granger causality exists between Bitcoin prices, inflation, and EPU and confirms that M2 affects Bitcoin prices, (iii) a nonlinear bidirectional causality exists between Bitcoin prices and inflation. While Bitcoin prices Granger cause M2 in the U.K. and Japan, inflation shows a nonlinear Granger causality with EPU in Japan. These findings help investors make investment decisions while considering the effects of M2, inflation, and EPU, and monetary authorities and policymakers make policies involving Bitcoin.
To Trung Thanh, Lê Thanh Hà, Nguyễn Thị Thanh Huyền, Tran Anh Ngoc
In this paper, we employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to study interlinkages between the cryptocurrency and Vietnam’s stock market by characterizing their connectedness starting from January 1, 2018, to December 31, 2021. We report that the COVID-19 health shocks impact the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that the cryptocurrency market significantly impacts Vietnam’s stock market, especially those with the largest market capitalization like Bitcoin and Ethereum. This market can be held accountable for Vietnam’s stock market volatility. In encountering the COVID-19 pandemic, the effect of the three cryptocurrencies reduced before 2020, around the end of 2019 and the beginning of 2020. However, from the end of 2020–2021, while cryptocurrencies continued their roles as net transmitters for Vietnam’s stock market.
Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.
Energy production is a phenomenon that has always preserved its importance for the history of humanity, as well as where the energy is spent and its consumption are also important. This study examined the causality relationship between Bitcoin energy consumption and Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise has been taken into account to represent technology companies’ stock market. In the analysis, daily price data for the period 12.02.2017-07.02.2021 were used. Toda-Yamamoto (1995) symmetric causality test and Hatemi-J (2012) asymmetric causality test were used for used to determine the relationship between Bitcoin energy consumption and technology companies’ stock values. According to the results of the Toda-Yamamoto (1995) causality test, it has been found that there is a causality from Bitcoin energy consumption to Apple's stock value; according to the Hatemi-J (2012) asymmetric causality test results, it has been determined that there is a causality from Bitcoin energy consumption positive shocks to Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise stock values negative shocks and from Bitcoin energy expenditure negative shocks to Hewlett Packard Enterprise negative shocks. According to the results of the study in general, it is seen that the change in Bitcoin energy consumption has an effect on the firm returns of the companies that sell the necessary tools for bitcoin energy production. From this, it can be commented that bitcoin mining is also effective on the stock returns of technology companies as well as many financial factors.
The present study is a novel attempt to unravel the connectedness of the green bond with energy, crypto, and carbon markets using the S&P green bond index (RSPGB). We consider MAC global solar energy index (RMGS) and ISE global wind energy index (RIGW) as proxies of the energy market and use bitcoin and the European energy exchange carbon index (REEX) for the cryptocurrency and carbon market. Employing the Diebold and Yilmaz (2012), Baruník and Krehlík (2018), and wavelet coherence econometric techniques, we find that the energy market (RMGS) has the highest connectedness derived from other asset classes, and bitcoin (RBTC) has the least connectedness. Concurrently, we find that the risk transmission is heterogeneous in different scales as the short period has less connectedness than the medium and long run. We conclude that the overall diversification opportunity among green bonds, energy stock, bitcoin, and the carbon market is more in the short-run than in the medium and long-run. In summary, our findings on the green bond market will provide investors, portfolio managers, and policymakers with critical insight into ensuring a sustainable financial market.
In this study, it was aimed to examine that the relationship between Bitcoin price, Bitcoin volume and bitcoin energy consumption, and to research leading indicators of Bitcoin and whether the stock markets of the 5 countries that produce the most Bitcoin act together. In this context, 2011-2022 monthly data of Bitcoin energy, Bitcoin price, Bitcoin volume, USA, China, Kazakhstan, Russia, and Canada indexes was regarded in the study. Diebold and Yilmaz (2012) spillover index and time varying parameter VAR (TVPVAR) methodologies were used in the study. In the result of Diebold and Yilmaz (2012) spillover methodology was observed that the spillover effect of the Bitcoin energy variable on the Bitcoin price is 3.5%. While was observed from leading indicators of Bitcoin to all series examined be spillover, the most spillover was observed that be from Bitcoin price to SP500 index. Net spillover index of Diebold and Yilmaz (2012) was calculated that is 4.54%. In addition to this, in the TVPVAR established model was examined the action and the reaction functions in 4, 8 and 12 months periods. In the action-reaction functions of the TVPVAR model, it was observed that the shocks at the 4, 8 and 12-month periods in Bitcoin energy prices spread with a similar intensity to the Bitcoin price. In the result of the study was observed that Bitcoin energy shocks spread to SP500, Shanghai, Kase and RTSI indexes in all periods, the shocks of the price and the volume shocks spread to these indexes in short periods.
Fatih Ecer, Adem Böyükaslan, Sarfaraz Hashemkhani Zolfani
Blockchain technologies, which form the basis of Industry 4.0, paved the way for cryptocurrencies to emerge as technological innovation in the technology age. Recently, investors worldwide have been interested in cryptocurrencies with increasing acceleration due to high earning expectations though they have no backing and intrinsic value. As such, this paper seeks to identify the most proper cryptocurrencies from an investment standpoint in our technological era. Fifteen well-known cryptocurrencies with the highest market capitalization are evaluated as per sixteen factors. An intuitionistic fuzzy set-driven methodology incorporating Evaluation Based on Distance from Average Solution (EDAS), Multi-Attributive Ideal Real Comparative Analysis (MAIRCA, and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), which is the study’s prominent novelty, has been applied to provide a strong group decision vehicle for cryptocurrency selection. Notwithstanding, although the results obtained with the three approaches are highly consistent, investors would not like to doubt the instrument they will invest in. The Borda count is then applied to obtain a compromise for the rankings obtained from each approach. As per our findings, Ethereum, Tether, and Bitcoin are the most suitable cryptocurrencies, whereas reliable software, ease of inclusion in the wallet, and stability are the foremost factors to consider when investing in cryptocurrencies. The findings are further discussed in detail from a financial perspective. The proposed approach could be employed to select different investment instruments in future studies.