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.     

Non-linear relationship between oil and cryptocurrencies: Evidence from returns and shocks

The price instabilities between oil prices and cryptocurrencies have motivated the current study to examine the nonlinear relationship between oil returns/shocks and cryptocurrencies during March 3, 2018 to October 10, 2021. We employed a novel methodology of cross-quantilogram to unveil the nonlinearity and asymmetry between oil shocks and cryptocurrencies. We find that when markets are normal and bullish, there is a positive correlation between oil returns and cryptocurrency returns at first lag; however, there is a negative correlation between oil returns and cryptocurrencies in all market conditions. Moreover, rising fluctuations in oil demand shocks brings significant movement in cryptocurrency returns in bearish market conditions and it is unlikely that oil demand shocks and cryptocurrencies returns move in same directions. Given these results, we proposed useful implications for policymakers, strategists, regulators, financial market participants, and investors to hedge/diversify their risk.     

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.     

When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic

This paper examines the dynamic spillovers among the major cryptocurrencies under different market conditions and accounts for the ongoing COVID-19 health crisis. We also investigate whether cryptocurrency policy (CCPO) uncertainty and cryptocurrency price (CCPR) uncertainty affect the dynamic connectedness. We adopt the Quantile-VAR approach to capture the left and right tails of the distributions corresponding to return spillovers under different market conditions. Generally, cryptocurrencies show heterogeneous responses to the occurrence of the COVID-19 pandemic. We find that the total spillover index (TCI) varies across quantiles and rises widely during extreme market conditions, with a noticeable impact of the COVID-19 pandemic. Bitcoin lost its position as a dominant “hedger” during the health crisis, while Litecoin became the most dominant “hedger” and/or “safe-haven” asset before and during the pandemic period. Moreover, our analysis shows a significant impact of market uncertainties on total and net connectedness among the five cryptocurrencies. We argue that the COVID-19 pandemic crisis plays a vital role on the relationship between CCPO as well as CCPR and the dynamic connectedness across all market conditions.     

A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.     

Return spillover analysis across central bank digital currency attention and cryptocurrency markets

Many central banks have now developed their digital currencies in response to the challenges posed by the proliferation of decentralised digital cryptocurrencies. However, little is known about the effects of the introduction of central bank digital currencies (CBDCs) on extant digital cryptocurrencies. This paper, therefore, aims to identify both the time- and frequency-domain spillover effects among cryptocurrency markets and a newly developed central bank digital currencies attention index (CBDCAI) by using two TVP-VAR-based spillover models. Our results demonstrate that CBDC attention significantly impacts cryptocurrency markets. Also, most investors in cryptocurrency markets are more likely to trade in the short term. The results of this study contribute to helping investors and investment institutions effectively avoid investment risks, reduce losses, and predict the return of some cryptocurrencies. Also help policymakers better understand the impact of markets and policies, and provide a reference for them to formulate policies.     

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.     

Does Bitcoin still own the dominant power? An intraday analysis

The study investigates the intraday dynamics and price patterns of the primary cryptocurrencies. The Granger Mackey-Glass (M-G) model is employed to examine the asymmetric and nonlinear dynamic interactions in the first moment using positive and negative returns. The bivariate BEKK-GARCH model is applied to identify cross-market volatility shocks and volatility transmissions in the cryptocurrency market. The intra-cryptocurrency market analysis reveals that Bitcoin contains predictive information that can nonlinearly forecast the performance of other digital currencies when cryptocurrency prices either are rising or declining. The dominant power of Bitcoin is not dismissed using the intraday data. Further, Bitcoin's intraday lagged shocks and volatility induces more rapid and destabilizing effects on the conditional volatility of other currencies than each of the other currencies does on BTC's conditional volatility. The virtual currency markets are dynamically correlated and integrated through first and second-moment spillovers.     

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.     

Asymmetric interdependencies between large capital cryptocurrency and Gold returns during the COVID-19 pandemic crisis

This article explores asymmetric interdependencies between the twelve largest cryptocurrency and Gold returns, over the period January 2015 – June 2020 within a NARDL (nonlinear autoregressive distributed lag) framework. We focus our analysis on the epicentre of the first wave of the COVID-19 pandemic from March 2020 to June 2020. During this crisis, cryptocurrencies are more correlated and more of them have returns that are cointegrated with Gold returns. Moreover, cryptocurrencies develop a long-term as well as a short-term asymmetric response to Gold returns during the COVID-19 period where most cryptocurrency returns respond more to negative changes and exhibit more persistence with Gold returns. Overall, our most important result confirms that the connectedness between Gold price returns and cryptocurrency returns increase in economic turmoil, such as during the COVID-19 crisis.     

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.     

Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks

Previous literature shows that major cryptocurrencies exhibit inverse asymmetric volatility: positive shocks increase price volatility more than negative ones. In this study, we revisit the asymmetric volatility dynamics of major cryptocurrencies using asymmetric GARCH models that incorporate endogenously detected structural breaks. Our results show that after incorporating structural breaks, volatility persistence decreases and asymmetric volatility increases for all cryptocurrencies in this study. Thus, prior research that ignores structural breaks underestimates the impact of unexpected news on price volatility in cryptocurrency markets. We also present important economic implications of our results: ignoring structural breaks adversely affects the hedging strategies, derivatives valuations, and risk exposure measurement of investors in cryptocurrency markets.     

“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.     

On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia

This paper categorizes Australian listed cryptocurrency-linked stocks (CLS) by their involvement as a user, developer and diffuser, and investor of blockchain technology and cryptocurrencies based on company announcements and published information on the company websites. By distinguishing CLS engagement with blockchain technology, we examine their returns and volatility spillover with the cryptocurrency market over the period 1 September 2017 to 7 June 2018, spanning important episodes and dynamics in the cryptocurrency market in 2017-2018, and the emergence of Australian CLS. Utilizing the Diebold and Yilmaz (2012) spillover methodology, we find significant unidirectional return spillover and weak volatility spillover from the cryptocurrency market to CLS, after controlling return dynamics of the Australian dollar, Gold and commodity. However, CLS with high involvement in blockchain technology displays stronger connectedness to the cryptocurrency market through return spillover relative to low involvement CLS. Our findings indicate that investors incorporate the price dynamics of cryptocurrencies into their trading decisions for CLS.     

Conditional tail-risk in cryptocurrency markets

In this paper we use $CoVaR$ to estimate the conditional tail-risk in the markets for bitcoin, ether, ripple and litecoin and find that these cryptocurrencies are highly exposed to tail-risk within cryptomarkets, while they are not exposed to tail-risk with respect to other global assets, like the U.S. equity market or gold. Although cryptocurrency returns are highly correlated one with the other, we find that idiosyncratic risk can be significantly reduced and that portfolios of cryptocurrencies offer better risk-adjusted and conditional returns than individual cryptocurrencies. These results indicate that portfolios of cryptocurrencies could offer attractive returns and hedging properties when included in investors’ portfolios. However, when we account for liquidity, the share of crypto assets in investors’ optimal portfolio is small.     

Up or down? Short-term reversal,momentum, and liquidity effects in cryptocurrency markets

We demonstrate a new powerful predictive signal for cryptocurrency returns: the last day's return. Based on daily prices of more than 3600 coins, we document that the cryptocurrencies with low last day's return significantly outperform their counterparts with high last day's return. The effect is confirmed by a battery of cross-sectional tests and portfolio sorts, and is not subsumed by a broad range of other return predictors. We argue that the daily reversals result from the illiquidity of the vast majority of traded cryptocurrencies. In consequence, the pattern is cross-sectionally dependent on liquidity, and the handful of largest and most tradeable coins exhibit daily momentum rather than a reversal. Our findings help to reconcile earlier conflicting evidence on return persistence in cryptocurrency markets.     

Cryptocurrency price prediction using traditional statistical and machine-learning techniques: A survey

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.     

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.     

Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market

Research on human attention indicates that objects that stand out from their surroundings, i.e., salient objects, attract the attention of our sensory channels and receive undue weighting in the decision-making process. In the financial realm, salience theory predicts that individuals will find assets with salient upsides (downsides) appealing (unappealing). We investigate whether this theory can explain investor behaviour in the cryptocurrency market. Consistent with the theory's predictions, using a sample of 1738 cryptocurrencies, we find that cryptocurrencies that are more (less) attractive to “salient thinkers” earn lower (higher) future returns, which indicates that they tend to be overpriced (underpriced). On average, a one cross-sectional standard-deviation increase in the salience theory value of a cryptocurrency reduces its next-week return by 0.41%. However, the salience effect is confined to the micro-cap segment of the market, and its size is moderated by limits to arbitrage.