In this paper, we examine the presence of herding in cryptocurrency market for four distinct sub-periods (Pre and During COVID-19 period, bear and bull markets) using daily closing prices of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, XRP, Stellar and Tether) from April 20, 2019 to January 31, 2021. The study employs cross-sectional absolute deviations (CSAD) model to test herd behavior and the results of the study provide evidence of herd behavior in the whole market for the selected period under study. The study also proofs the presence of herding during COVID-19 period and in positive market returns. These indicate that, investors in the cryptocurrency market, during COVID-19 periods, and in bullish market are inclined to the investment behavior of other peer investors in the market. The study is significant to investors, regulators and players in the cryptocurrency market so as to deepen their understanding of herding behavior since herding is thought to increase the volatility of the market. The study is significant to investors, regulators and players in the cryptocurrency market so as to deepen their understanding of herding behavior since herding is thought to increase the volatility of the market.
The gold mine has been a commodity used for thousands of years, today it is also an investment tool with the highest reliability. However; cryptocurrencies that are recently used are affecting our portfolio. Bitcoin is the most traded cryptocurrency. Since there are alternative investment instruments involved in portfolios, the relationship between these two independent values inspired the emergence of this study. The aim of this study was to investigate whether there is a causality-cointegration relationship between daily Bitcoin prices and gold prices for the periods between 10,01,2014 and 11,12,2020. In the application section, Toda Yamamoto causality and the Maki Cointegration test were applied. According to the results of the Toda Yamamoto causality test, there is a two-way causality relationship. According to the results of the Maki cointegration test, there was no long-term relationship between the series. As a result, it is expected that in the long term, investors will have a risk-reducing effect by including both investment instruments in the same portfolio.
This study uses a novel perspective to examine the causal connectedness between green bonds and other conventional assets, including clean energy, price of CO2 emission allowances, Bitcoin, and the S&P 500 stock market covering from January 2013 to March 2019. We apply the Multilayer Perceptron Neural Network Non-linear Granger causality and Transfer Entropy to detect possible changes in the causal direction between green bonds and other considered variables. We find a bidirectional relationship between green bonds, S&P 500, and Bitcoin markets, while green bonds have a unidirectional connection with the price of CO2 emission allowances.
Francisco Jareño, María de la O González, Raquel López, Ana Rosa Ramos
This study explores potential non-linear and asymmetric interdependencies between oil price shocks and leading cryptocurrency returns. In addition, this research splits changes in crude oil prices into three relevant components: risk, demand, and supply shocks. By applying the NARDL methodology, this paper examines the connection between oil and cryptocurrencies in the period between November 20, 2018 and June 30, 2020, conducting a study of the first wave of the COVID-19 pandemic. Our results confirm that demand shocks show the greatest connection with the returns of the cryptocurrencies analysed. In addition, both short-term and long-term results show a greater interdependence between oil and cryptocurrencies in periods of economic turbulence, such as the SARS-CoV-2 coronavirus crisis.
This study examines the connectedness between the US yield curve components (i.e., level, slope, and curvature), exchange rates, and the historical volatility of the exchange rates of the main safe-haven fiat currencies (Canada, Switzerland, EURO, Japan, and the UK) and the leading cryptocurrency, the Bitcoin. Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility. The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks. Meanwhile, the curvature of the yield curve and the Japanese Yen, Swiss Franc, and British Pound act mainly as net receivers. Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve's level, slope, and curvature, and from any main currency investigated. These findings hint that Bitcoin might provide hedging benefits. However, similar to the static analysis, our dynamic analysis shows that during different periods and particularly in stressful times, Bitcoin is far from being isolated from other currencies or the yield curve components. The dynamic analysis allows us to observe Bitcoin's connectedness in times of stress. Evidence supporting this contention is the substantially increased connectedness due to policy shocks, political uncertainty, and systemic crisis, implying no empirical support for Bitcoin's safe-haven property during stress times. The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times, Bitcoin has the property of a diversifier. The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.
Chidi U. Okonkwo, Bright O. Osu, Farid Chighoub, Ben I. Oruh
This paper investigated the co-movement between the bitcoin (BTC) and the exchange rates of some African currencies to the USD (United States Dollars) using the continuous wavelet transform (CWT) and wavelet coherence (WTC). This was done for the noisy as well as the denoised series. The CWT for the noisy series suggests high volatility for those who hold the currencies for the short term and low volatility for those who hold the currencies for a long-term period. The CWT of the denoised series suggests that volatility at low frequency is driven by noise, while volatility at a higher frequency is driven by market forces. The wavelet coherence suggests that in the presence of noise, bitcoin will be a hedge for the currencies. However, in the absence of noise, bitcoin is a haven for the Egyptian EGP, followed by the Algerian DZD, then the Nigerian NGN, and may not be a haven for the South African ZAR.
This study examines how Bitcoin’s trading characteristics react to the COVID-19 pandemic, using detailed futures trading data from the Chicago Mercantile Exchange. The results show that volume-weighted Bitcoin futures return responds positively to the spikes of public interest. Meanwhile, the surges of pandemic information do not harm market quality. Volume, bid-ask spread, and trading frequency remain stable, indicating that the positive price reaction is not a result of a few small uninformed trades. Bitcoin's conditional beta on the S&P 500 index drops to near zero, while the conditional beta on gold more than doubles. These results indicate that traders have been using Bitcoin as a safe-haven asset after the pandemic outbreak.
Ghassen El Montasser, Lanouar Charfeddine, Adel Benhamed
This paper compares the degree of cryptocurrency market efficiency during the pre- and post COVID-19 pandemic with the bubble and non-bubble periods of cryptocurrency markets. Furthermore, it examines and clusters eighteen cryptocurrencies by exploring their market efficiency similarity. Comparing the cryptocurrency bubble periods with the COVID-19 pandemic, the results indicate that this pandemic has the highest impact on cryptocurrency market efficiency. Interestingly, using the dynamic time warping clustering approach, we found evidence on the presence of three clusters that essentially represent mining coins, non-mining coins and token categorizations .
The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.
We employ the quantile-coherency approach and causality-in-quantile method to revisit the roles of Bitcoin, U.S. dollar, crude oil and gold for USA, Chinese, UK, and Japanese stock markets. The main results show that the impact of global financial assets varies across different investment horizons and quantiles. We find that in most cases, the correlation between global financial assets and stock indexes is not significant or is weakly positive. From the perspective of investment horizons (frequency domain), the correlation in the short term is mostly manifested in Bitcoin, while in the medium and long term it is shifted to dollar assets. At the same time, the relationships are significantly higher in the medium and long term than in the short term. From the point of view of quantiles, it shows a weak positive correlation at the lower quantile. However, the correlation between the two is not significant at the median quantile. At the high quantiles, there is a weak negative linkage. According to the causality-in-quantiles approach results, in most cases global financial assets have different degrees of predictive capacity for the selected stock markets. Especially around the median quantile, the predictive ability was strongest.
Purpose: This study aims to investigate the causal relationships between Bitcoin prices and developed and developing country stock markets. Design/methodology/approach: In the analysis part of the study, the causality test developed by Findings: As a result of the analysis, a two-way causality was found between BTC and DJI, among the developed country stock markets. On the other hand, there was a causality relationship from FCHI to BTC, while there was no causality from BTC to FCHI. There was a causality relationship from BTC to N225, while there was no causality from N225 to BTC. Finally, no causality relationship was found between DAX and BTC. Looking at the developing country stock markets, however, there was no causality relationship from BIST to BTC, there was a causality relationship from BTC to BIST. There was no causality relationship from BVSP to BTC, but there was a causality relationship from BTC to BVSP. There was no causality from MOEX to BTC, but there was a causality relationship from BTC to MOEX. There was no causality from BSE to BTC, but it was found that there was a causality relationship from BTC to BSE. As can be seen from the results, it is seen that Bitcoin prices are the cause of the stock markets of developing countries. It has been determined that Bitcoin historical values are effective on BIST, BOVESPA, MOEX Russia and BSE Sensex 30. The findings of the study were discussed in the results section. Originality/value: It is of great importance for investors to follow the developments in the stock market indices subject to research simultaneously with the Bitcoin prices. It is important that investors who will invest in these markets do not ignore the relationship between these markets in portfolio diversification.
Elie Bouri, Rangan Gupta, Chi Keung Marco Lau, David Roubaud
We study whether level of risk aversion can be used to predict Bitcoin returns using copulas and quantile-based models. We find evidence of predictability when the market return is at extreme quantiles. Further analyses show that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. The predictability is positive when the risk aversion is at very low level. However, predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that when risk aversion and Bitcoin returns are at very high levels, Bitcoin is less likely to have large gains.
Toan Luu Duc Huynh, Rizwan Ahmed, Muhammad Ali Nasir, Muhammad Shahbaz · 5 authors
In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.
Erdinc Akyildirim, Oğuzhan Çepni, Shaen Corbet, Gazi Salah Uddin
In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.
One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.
This letter revisits the time-series relation between cryptocurrency prices and forward inflation expectations. Using wavelet time-scale techniques, a positive link between cryptocurrencies and forward inflation rates is identified, focused on a brief period surrounding the onset of the COVID-19 pandemic. This coincides with a rapid and synchronized decrease in cryptocurrency prices and forward inflation expectations, followed by a swift recovery to pre-crisis levels. Outside of the crisis period, we find no clear evidence of any inflation hedging capacity of Bitcoin or Ethereum during times of increasing forward inflation expectations.
This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.
This study investigates asymmetric multifractality and market efficiency of the major cryptocurrencies during the COVID-19 pandemic while accounting for different investment horizons. By applying the asymmetric multifractal detrended fluctuation analysis, we show that the outbreak affected the efficiency property of price behaviors differently between short- and long-term horizons. After the outbreak, the markets exhibited stronger multifractality in the short-term but weaker multifractality in the long-term. We also analyze asymmetric market patterns between upward and downward trends and between small and large price fluctuations and confirm that the outbreak has greatly changed the level of asymmetry in cryptocurrency markets.
Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Miguel Ángel Echarte Fernández, Ricardo Francisco Reier Forradellas
There are different studies that point out that the price of electricity is a fundamental factor that will influence the mining decision, due to the cost it represents. There is also an ongoing debate about the pollution generated by cryptocurrency mining, and whether or not the use of renewable energies will solve the problem of its sustainability. In our study, starting from the Environmental Performance Index (EPI), we have considered several determinants of cryptocurrency mining: energy price, how that energy is generated, temperature, legal constraints, human capital, and R&D&I. From this, via linear regression, we recalculated this EPI by including the above factors that affect cryptocurrency mining in a sustainable way. The study determines, once the EPI has been readjusted, that the most sustainable countries to perform cryptocurrency mining are Denmark and Germany. In fact, of the top ten countries eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom); and the remaining two are Asian (South Korea and Japan).