Information transmission among energy markets,cryptocurrencies, and stablecoins under pandemic conditions

This paper investigates the directional causal relationship and information transmission among the returns of West Texas Intermediate (WTI), Brent, major cryptocurrencies, and stablecoins by drawing on daily data from July 2019 to July 2020. Applying effective transfer entropy, a non-parametric statistic, the results show that the direction of the causal relationship and the nature of information spillovers changed after the COVID-19 pandemic. More precisely, our findings reveal that WTI and Brent are leading the prices of Bitcoin and Bitcoin Cash. Conversely, Bitcoin futures and stablecoins (TrueUSD and USD Coin) are leading WTI and Brent prices. In addition, the stablecoin Tether became a leader against Brent prices after the pandemic, although it is still following WTI prices. Moreover, Ethereum and USD coin preserved their position as leaders against Brent prices. Interestingly, our results also reveal that Ethereum, Litecoin, and Ripple preserved their position as leaders of WTI prices. The change in the nature of directional causality and the spillover effect after the COVID-19 crisis provide valuable information for practitioners, investors, and policymakers on how the ongoing pandemic influences the connection and network correlation among the energy, cryptocurrency, and stablecoin markets.     

Cryptocurrencies and stock market indices. Are they related?

In this paper, we investigate the stochastic properties of six major cryptocurrencies and their bilateral linkages with six stock market indices using fractional integration techniques. From the univariate analysis, we observe that for Bitcoin and Ethereum, the unit root null hypothesis cannot be rejected; for Litecoin, Ripple and Stellar, the order of integration is found to be significantly higher than 1; for Tether, however, we find evidence in favour of mean reversion. For the stock market indices, the results are more homogeneous and the unit root cannot be rejected in any of the series, with the exception of VIX where mean reversion is obtained. Concerning bivariate results within the cryptocurrencies and testing for cointegration, we provide evidence of no cointegration between the six cryptocurrencies. Along the same lines, testing for cointegration between the cryptocurrencies and the stock market indices, we find evidence of no cointegration, which implies that the cryptocurrencies are decoupled from the mainstream financial and economic assets. The findings in this paper indicate the significant role of cryptocurrencies in investor portfolios since they serve as a diversification option for investors, confirming that cryptocurrency is a new investment asset class.     

Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.     

A systematic review of the bubble dynamics of cryptocurrency prices

This paper surveys the academic literature concerning the formation of pricing bubbles in digital currency markets. Studies indicate that several bubble phases have taken place in Bitcoin prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibit several bubble phases. The Augmented Dickey Fuller (ADF) as well as the Log-Periodic Power Law (LPPL) methodology are the most frequently employed techniques for bubble detection and measurement. Based on much academic research, Bitcoin appears to have been in a bubble-phase since June 2015, while Ethereum, NEM, Stellar, Ripple, Litecoin and Dash have been denoted as possessing bubble-like characteristics since September 2015. However, this latter group possess little academic evidence supporting the presence of bubbles since early 2018. An overall perspective is provided based on a robust bibliography based on large deviations of market quotes from fundamental values that can serve as a guide to policymakers, academics and investors.     

Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures

Using daily data from August 9, 2015, to July 7, 2020, this study examines the effects of economic policy uncertainty (EPU) on the returns of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. To this end, two new measures of EPU (Twitter-based economic uncertainty and Twitter-based market uncertainty) are considered. A Granger causality test using the recursive evolving window approach shows a significant causality between the Twitter-based EPU measures and the BTC/USD exchange rate from October 2016 to July 2017. Moreover, a significant causality was noted from the EPU measures to the ETH/USD exchange rate from June 2019 to February 2020 and from the EPU measures to the XRP/USD exchange rate from January 2020 to February 2020. The Twitter-based EPU measures primarily positively affect the returns of the related cryptocurrencies during these periods. These results are robust to different measures of Twitter-based EPU and different econometric techniques. Potential implications, including the COVID-19 era, are also discussed.     

Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19

In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate connectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dynamics of the crypto prices over time.     

Volatility cascades in cryptocurrency trading

This paper studies volatility cascades across multiple trading horizons in cryptocurrency markets. Using one-minute data on Bitcoin, Ethereum and Ripple against the US dollar, we implement the wavelet Hidden Markov Tree model. This model allows us to estimate the transition probability of high or low volatility at one time scale (horizon) propagating to high or low volatility at the next time scale. We find that when moving from long to short horizons, volatility cascades tend to be symmetric: low volatility at long horizons is likely to be followed by low volatility at short horizons, and high volatility is likely to be followed by high volatility. In contrast, when moving from short to long horizons, volatility cascades are strongly asymmetric: high volatility at short horizons is now likely to be followed by low volatility at long horizons. These results are robust across time periods and cryptocurrencies.     

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.     

Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis

This article examines the co-movement relationship among representative cryptocurrencies from the perspectives of returns and volatility. Wavelet coherence and the correlation network are introduced to explore the interdependence of cryptocurrencies, and then risk reduction and downside risk reduction are used to test the hedging effects of Bitcoin on others at different time frequencies. The empirical results provide evidence of co-movement and hedging effects. Additionally, positive correlations between Bitcoin and other cryptocurrencies exist on short-to-medium investment horizons. Moreover, both Bitcoin's returns and its volatility are ahead of other cryptocurrencies at low frequencies. In addition, a hedging effect across Bitcoin against other cryptocurrencies is more obvious in the long run. Furthermore, Bitcoin has hedging effects on other cryptocurrencies according to time-frequency horizons.     

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

A tale of two tails among carbon prices,green and non-green cryptocurrencies

This paper studies the tail dependence among carbon prices, green and non-green cryptocurrencies. Using daily closing prices of carbon, green and non-green cryptocurrencies from 2017 to 2021 and a quantile connectedness framework, we find evidence of asymmetric tail dependence among these markets, with stronger dependence during highly volatile periods. Moreover, carbon prices are largely disconnected from cryptocurrencies during periods of low volatilities, while Bitcoin and Ethereum exhibit time-varying spillovers to other markets. Our results also show that green cryptocurrencies are weakly connected to Bitcoin and Ethereum, and their net connectedness are close to 0, except during the COVID-19 pandemic. Finally, we find a significant influence of macroeconomic and financial factors on the tail dependence among carbon, green and non-green cryptocurrency markets. Our results highlight the time-varying diversification benefits across carbon, green and non-green cryptocurrencies and have important implications for investors and policymakers.     

Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic

The COVID-19 pandemic provided the first widespread bear market conditions since the inception of cryptocurrencies. We test the widely mooted safe haven properties of Bitcoin, Ethereum and Tether from the perspective of international equity index investors. Bitcoin and Ethereum are not a safe haven for the majority of international equity markets examined, with their inclusion adding to portfolio downside risk. Only investors in the Chinese CSI 300 index realized modest downside risk benefits (contingent on very limited allocations to Bitcoin or Ethereum). As Tether successfully maintained its peg to the US dollar during the COVID-19 turmoil, it acted as a safe haven investment for all of the international indices examined. We caveat the latter findings with a warning that Tether's dollar peg has not always been maintained, with evidence of impaired downside risk hedging properties earlier in our sample.     

A novel two-stage approach for cryptocurrency analysis

Modelling and quantifying the underlying characteristics of the cryptocurrency market has drawn increasing attention since Bitcoin went online in 2009. This study proposes a two-stage decomposition and composition method (2SDC) that begins with a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) for better interpreting cryptocurrency formations. This study involves daily closing price data from six cryptocurrencies (i.e., Bitcoin, Ethereum, Bitcoin Cash, Litecoin, Monero and Dash) from July 23rd, 2017 to July 23rd, 2019. In the first stage, six time series are jointly decomposed into 10 independent intrinsic mode functions (IMF) from high to low frequency plus one residual. In the second stage, the IMFs for each cryptocurrency are composed into three components based on Wilcoxon signed-rank test, including high and low frequency components and a long-term trend. These three multi-scale components can be interpreted as short-term fluctuations caused by investor sentiment and micro-structure, the effect of significant events and fundamental values. Furthermore, we demonstrated that the low and high frequency compositions are determining factors of cryptocurrency prices, which supports for the existing evidence (e.g. Bouoiyour, Selmi, Tiwari, & Olayeni, 2016; Ji, Bouri, Lau, & Roubaud, 2019).     

Directional predictability from central bank digital currency to cryptocurrencies and stablecoins

This paper investigates the interaction and the directional predictability between the central bank digital currencies (CBDCs) and the major cryptocurrencies and stablecoins during the period between 17 May, 2019–31 December, 2021. To this aim, we employ the "Cross-Quantilogram” model, to examine how and whether the traditional digital currencies react to the CBDC uncertainty and attention shocks. Our findings suggest that CBDC uncertainty index is negatively related to cryptocurrency and stablecoin returns. Furthermore, the CBDC attention index is negatively associated with Bitcoin, Ethereum, XPR and Terra USD, however, it is positively related to Tether, Binance, USD Coin and Dai. Our results are useful for regulators, investors and policy makers, to understand and assess the potential effect of CBDC adoption news on the volatility of the stablecoins and traditional cryptos.     

Re-evaluating portfolio diversification and design using cryptocurrencies: Are decentralized cryptocurrencies enough?

This paper investigates the portfolio diversification potential of a pool of cryptocurrencies classified based on their degree of leadership. We employ the mean-variance and the higher-order moments optimization approaches to evaluate the diversification potential of centralized and decentralized cryptocurrencies across multiple frameworks. While theoretical implications of the mean-variance and the higher-order moments optimization approaches are similar, our results suggest that the latter provides a more precise portfolio allocation strategy because it considers investor risk-aversion for each moment. Furthermore, we find that extending the pool of cryptocurrencies achieves marginal diversification benefits due to considerable co-movements among the cryptocurrencies. Moreover, we find that decentralized cryptocurrencies offer greater diversification potential than centralized cryptocurrencies, although centralized cryptocurrencies carry some diversification potential during alt-seasons. In order of their weights, Bitcoin, Chainlink, and Ethereum (all decentralized) offer the highest contribution to portfolio diversification across most portfolio frameworks, while Ethereum offers greater diversification benefits during the alt-seasons.     

News sentiment in the cryptocurrency market: An empirical comparison with Forex

We use high frequency intra-day data to investigate the influence of unscheduled currency and Bitcoin news on the returns, volume and volatility of the cryptocurrency Bitcoin and traditional currencies over the period from January 2012 to November 2018. Results show that Bitcoin behaves differently to traditional currencies. Traditional currencies typically experience a decrease in returns after negative news arrivals and an increase in returns following positive news whereas Bitcoin reacts positively to both positive and negative news. This suggests investor enthusiasm for Bitcoin irrespective of the sentiment of the news. This phenomenon is exacerbated during bubble periods. Conversely, cryptocurrency cyber-attack news and fraud news dampen this effect, decreasing Bitcoin returns and volatility. Our results contribute to the discussion on the nature of Bitcoin as a currency or an asset. They further inform practitioners about the characteristics of cryptocurrencies as a financial asset and inform regulators about the influence of news on Bitcoin volatility, particularly during bubble periods.     

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.     

The entry and exit dynamics of the cryptocurrency market

This paper presents an analysis of the entry and exit dynamics of the cryptocurrency market that focuses on the growth of initial coin offerings during 2015–2020. We used two different datasets: one includes long-lived cryptocurrencies, while the other includes the whole cryptocurrency system at our disposal–that is, it considers the entering and exiting cryptocurrencies. Comparing the dynamics between both datasets with the index cohesive force approach, we assessed how the growth of the initial coin offerings and the exiting cryptocurrencies affected the connectedness of the market. Our results show that the expansion of the cryptocurrency system gave rise to a strong collective movement during 2018–2019. Afterwards, the group pressure, due to the bubble of the initial coin offerings, decreased in favour of the largest cryptocurrencies. Lastly, we observed changes in the hierarchical order of the most influential cryptocurrencies. In particular, Ethereum became the most influential cryptocurrency, at the detriment of Bitcoin.     

“Shiny” crypto assets: A systemic look at gold-backed cryptocurrencies during the COVID-19 pandemic

In this paper, we empirically analyse the performance of five gold-backed stablecoins during the COVID-19 pandemic and compare them to gold, Bitcoin and Tether. In the digital assets' ecosystem, gold-backed cryptocurrencies have the potential to address regulatory and policy concerns by decreasing volatility of cryptocurrency prices and facilitating broader cryptocurrency adoption. We find that during the COVID-19 pandemic, gold-backed cryptocurrencies were susceptible to volatility transmitted from gold markets. Our results indicate that for the selected gold-backed cryptocurrencies, their volatility, and as a consequence, risks associated with volatility, remained comparable to the Bitcoin. In addition, gold-backed cryptocurrencies did not show safe-haven potential comparable to their underlying precious metal, gold.