Search

Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors. This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies, Bitcoin and Ethereum, and the factors that drive efficiency. It uses daily data from August 7, 2016, to February 15, 2023, the adjusted market inefficiency magnitude (AMIMs) measure, and quantile regression. The results show evidence of time variation in the levels of market (in)efficiency for Bitcoin and Ethereum. Interestingly, the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles. Notably, cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of (in) efficiency, whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient. Finally, the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles. [ABSTRACT FROM AUTHOR]

The cryptomarket has evolved into a complex system of different types of cryptoassets, each playing an important role within the system. These can be clustered into four main groups: market spillovers, portfolio management, herding and market crashes, and regulation. The above-discussed high connectedness of the cryptomarkets raises alarms with possible market implosions and cascades of losses across the markets, often materialising in a herding behaviour of market participants. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation. [ABSTRACT FROM AUTHOR]

Are price discontinuities in cryptocurrencies jointly related to large swings in geopolitical risk? This is a relevant question to answer given recent news from the press that Bitcoin's price jumps are driven by jumps in the level of geopolitical risk index. To answer this question, we examine first the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and then study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Our dataset is at the daily frequency and covers the period 30 April 2013 to 31 October 2019. The results show that the price behaviour of all cryptocurrencies under study is jumpy but only Bitcoin jumps are dependent on jumps in the geopolitical risk index. This revealed evidence of significant co-jumps for the case of Bitcoin only nicely complements previous studies arguing that Bitcoin is a hedge against geopolitical risk. [ABSTRACT FROM AUTHOR]

We examine the presence of outliers and time-varying jumps in the returns of four major cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, Litecoin), and a broad cryptocurrency index (CCI30). The results indicate that only Bitcoin returns are contaminated with outliers. Time-varying jumps are present in Bitcoin, Litecoin, Ripple, and the cryptocurrency index. Notably, the presence of jumps in Bitcoin is significant after correcting for outliers. The main findings point to a price instability in some major cryptocurrencies and thereby the importance of accounting for large shocks and time-varying jumps in modelling volatility in the debatable cryptocurrency markets. [ABSTRACT FROM AUTHOR]

We compare the weak/strong hedging abilities of three alternative assets, namely bitcoin, gold and US VIX futures, against the downside movements in BRICS stock market indices. Results from the cross‐quantilogram approach indicate that bitcoin and gold are weak hedges. Analysis from the recursive sampling shows that each of bitcoin, gold and VIX futures has a time‐varying hedging role in some BRICS countries, which has been shaped by the COVID‐19 outbreak. Results from the conditional diversification benefits show appealing roles for the three alternative assets for investors in BRICS stock markets. However, gold appears to have higher and more stable diversification benefits in China, especially during the COVID‐19 outbreak. Conversely, VIX futures offer higher diversification benefits in Brazil, Russia, India and South Africa during the abrupt of the COVID‐19 outbreak. [ABSTRACT FROM AUTHOR]

• Study co-jumps using a network model from May 15, 2018 to June 15, 2023. • Co-jump is heterogeneous across cryptocurrencies and varies over time. • Bitcoin is very influential. It dominates the centrality ranking during COVID-19. • Some smaller cryptocurrencies are also influential during the pandemic. • Portfolios constructed based on the co-jump network beat the baseline strategy. We examine the co-jumps of 37 cryptocurrencies based on a network model, and analyse the portfolio implications. The results reveal that, firstly, Bitcoin exerts the strongest influence. Secondly, co-jump heterogeneity exists across pairs of cryptocurrencies with different market-capitalizations, and the impact of co-jumps is time-varying. Thirdly, the dynamic ranking of co-jump influence shows that, during the COVID-19 pandemic, Bitcoin dominates in the centrality ranking. However, smaller cryptocurrencies (Dogecoin and TRON) exhibit significant co-jump influence. Fourthly, the portfolios constructed based on the co-jump network outperform the baseline strategy by attaining higher returns while experiencing less volatility and shorter downside periods. [ABSTRACT FROM AUTHOR]

The price behavior of cryptocurrencies relative to conventional currencies remains relatively understudied, despite some attempts that consider the Bitcoin market. This study examines long-range cross-correlations and asymmetric multifractality between leading conventional currencies (Swiss Franc, Euro, British Pound, Yen, and Australian dollar) and main cryptocurrencies (Bitcoin, Litecoin, Ripple, Monero, and Dash) from June 2, 2014 to February 28, 2018. Empirical results show evidence of a significant asymmetric characteristic from the cross-correlation, that is found to be persistent and multifractal in most of the cases. Bitcoin and Litecoin are the cryptocurrencies that exhibit the most multifractal behavior, whereas Monero and Ripple generally exhibit lower multifractal behavior. All cryptocurrencies exhibit a slightly lower asymmetry for longer terms. • Study asymmetric cross-correlations between main currencies and cryptocurrencies. • Apply MF-ADCCA and show that cross-correlations exhibit multifractal features. • Find evidence of asymmetric multifractality in many cases. • Show that Bitcoin and Litecoin exhibit a high multifractal behavior. • Show that Monero and Ripple exhibit a low multifractal behavior. [ABSTRACT FROM AUTHOR]

• Conduct a broad study of systemic risk in the crypto markets around the FTX Collapse. • Use Rotated Gumbel copula and GARCH copula quantile regression based ΔCoVaR model. • Find strong risk spillover effects from FTX Token to several cryptocurrencies. • During FTX fallout, Solana and Cardano received most risk spillover from FTX Token. • The stable coin, Tether, followed by Bitcoin, absorbed the least risk spillover. We examine the dynamic lower tail dependence and downside risk spillover between the FTX Token and seven major cryptocurrencies using Rotated Gumbel copula and GARCH copula quantile regression-based ∆CoVaR models. Daily data is analyzed from May 1, 2020 to December 31, 2022. The results show a strong evidence of risk spillover effects from FTX Token to crypto markets. Solana, followed by Cardano, displays the largest downside risk spillover. Tether and Bitcoin are affected least by the FTX fallout, receiving the lowest downside risk spillovers. Furthermore, the dynamic risk spillover effects are heterogeneous over time and comparatively different for each cryptocurrency. [ABSTRACT FROM AUTHOR]

We investigate potential sources of emerging statistical arbitrage opportunities in the Bitcoin market across five exchanges – Binance, Bitfinex, Bitstamp, Coinbase, and Kraken – via the instrumental variables approach to control for apparent endogeneity. We show that arbitrage opportunities arise when the network is congested and Bitcoin prices are volatile. Increased exchanges volume and on-chain activity increase the correlation between exchanges and thus reduce the arbitrage opportunities. These outcomes are intuitive and economically valid which supports the notion that Bitcoin market is highly volatile and risky but its behavior follows standard economic and financial intuition. • Sources of arbitrage opportunities in Bitcoin markets are explored. • Instrumental variables approach is utilized to control for endogeneity. • Network congestion and price volatility allow arbitrage opportunities to emerge. • Increased exchanges volume and on-chain activity suppress arbitrage opportunities. [ABSTRACT FROM AUTHOR]

We examine the price disorder and informational efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during the COVID-19 pandemic. In this sense, we estimate the permutation entropy and Fisher information measure (FIM). We use these complexity measures to construct the Shannon–Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply the sliding time window approach to study the temporal evolution of informational efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano was the most efficient cryptocurrency. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryptocurrencies' usage for liquidity risk diversification strategy. • We investigate the price disorder and market efficiency by considering five relevant records of cryptocurrencies. • Our analysis encompasses two periods, before and during the COVID-19 pandemic. • The results provide new relevant insights into the benefits of using cryptocurrencies linked. [ABSTRACT FROM AUTHOR]

• We detect that both asset classes are subject to jump behaviour. • The jumps in the cryptocurrencies increases the likelihood of jumps in the blockchain exposed companies. • The co-jumping behaviour is not affected by the COVID-19 outbreak. We examine whether the occurrence of jumps in the return of major cryptocurrencies increases the likelihood of jumps in the stock returns of blockchain and crypto-exposed US companies. We use two criteria to identify the US stocks with blockchain and cryptocurrency exposure; i) text search and ii) membership in the blockchain indices. We first detect that both asset classes are subject to jump behaviour. Then, we employ logistic regressions and show that the occurrence of jumps in some cryptocurrencies increases the probability of jumps in several blockchain and crypto-exposed companies. The co-jumping behaviour is not affected by the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

• We forecast the returns of top five major cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin. • The forecasting methods involve standard factor models and with regime-switching factor loadings. • Our results show that factor models outperform the benchmark random walk. • Regime-switching versions of forecasting models perform best in majority of the cases. The returns of cryptocurrencies tend to co-move, with their degree of co-movement being contingent on the (bullish- or bearish-) states. Given this, we use standard factor models and regime-switching factor loadings to forecast the returns of a specific cryptocurrency based on its lagged information and informational contents of 14 other cryptocurrencies, with these 15 together constituting 65% of the market capitalization. Considering top five cryptocurrencies namely, Bitcoin, Ethereum, Ripple, Dogecoin, and Litecoin, we find significant forecastability and evidence that factor models, in general, outperform the benchmark random-walk model, with the regime-switching versions standing out in the majority of the cases. [ABSTRACT FROM AUTHOR]

• Identify the major areas of empirical research on cryptocurrencies. • Construct the median and tail networks of top 50 cryptocurrencies. • Higher spillover in tails compared to median and highest at right tail. • Bitcoin is not the leader in risk transmission and reception. • Digitalcoin and GlobalBoost-Y are top risk transmitters and Litecoin is a receiver. We examine the median- and tail-based return interdependence among cryptocurrencies under both normal and extreme market conditions. Using daily data and combining the LASSO technique with quantile regression within a framework of network analysis, the main results show the following: Interdependence is higher at tails than at medians, especially the right tail. Bitcoin is not the leading risk transmitter or receiver, but this role is taken by smaller cryptocurrencies. The volatilities of market, size, and momentum drive return connectedness and clustering coefficients under both normal and extreme market conditions. Finally, profitable trading strategies are constructed and evaluated. [ABSTRACT FROM AUTHOR]

• Propose four-factor model of cryptocurrencies pricing by incorporating contagion risk • Estimate contagion measure for the large left-tail events in the idiosyncratic disturbances of cryptocurrencies • Empirically test the four-factor pricing model • Proposed model outperforms cryptocurrency-CAPM and three-factor models We examine as if the incorporation of the contagion risk, which is found significant in cryptocurrencies, can make the resulting four-factor pricing model offers an improved explanatory power. We estimate contagion measure for the large left-tail events in the idiosyncratic disturbances of cryptocurrencies and then incorporate it into the three-factor pricing model. Using data of 1,967 cryptocurrencies from January 1, 2015 to September 26, 2019, we show that the four-factor pricing model outperforms both the cryptocurrency-CAPM and three-factor models. Our findings are useful to researchers of cryptocurrency anomalies and those applying quantitative strategies in the cryptocurrency market. [ABSTRACT FROM AUTHOR]

In this study, we examine the asymmetric efficiency of cryptocurrencies using 1-hour data of Bitcoin, Ethereum, Litecoin, and Ripple. In doing so, we utilize the asymmetric multifractal detrended fluctuation analysis (MF-DFA). We find significant asymmetric multifractality in the price of cryptocurrencies and that upward trends exhibit stronger multifractality than downward trends. Using the time-varying deficiency measure, we show that the COVID-19 outbreak adversely affected the efficiency of the four cryptocurrencies, given a substantial increase in the levels of inefficiency during the COVID-19 period. Bitcoin and Ethereum are the hardest hit, and at the same time, these two largest cryptocurrencies recovered faster at the end of March 2020 from their sharp dip towards inefficiency. The findings confirm previous evidence that market efficiency is time varying; also, unprecedented catastrophic events, such as the COVID-19 outbreak, have adverse effects of on the efficiency of leading cryptocurrencies. • Study asymmetric efficiency of cryptocurrencies using asymmetric MF-DFA. • Find significant asymmetric multifractality in the price of cryptocurrencies. • COVID-19 outbreak adversely affected the efficiency of cryptocurrencies. • The efficiency of Bitcoin and Ethereum is the hardest hit. • The efficiency of these large cryptocurrencies recovered faster around March 2020. [ABSTRACT FROM AUTHOR]

• Tail dependence in the return-volume of leading cryptocurrencies is studied. • Various types of static and time-varying GARCH-copula models are applied. • Student- t and time varying SJC copulas are the best choices. • Extreme returns are associated with extreme trading volumes. • But, when returns and volumes are high, tail dependence is stronger than otherwise. We analyze the average and extreme dependence between returns and trading volumes of three main cryptocurrencies (Bitcoin, Ethereum and Litecoin) via GARCH-copula models. The copula models used allow for checking the dependence structure under various market conditions. The results indicate that the Student- t and time varying symmetrized Joe Clayton (SJC) copulas are the best choices for the three cryptocurrencies. The tail dependence of return-volume is asymmetric under Gumbel, Clayton and SJC copulas. Meanwhile, extreme returns are associated with extreme trading volumes, and tail dependence is stronger when returns and volumes are high than when returns and volume are low. [ABSTRACT FROM AUTHOR]

Blockchain exchange-traded funds (ETFs) are nascent products in the financial industry. A limited literature focuses on the multifractal analysis of some conventional ETFs, but the multifractal behaviour of the blockchain ETF market has not been studied. In this paper, we investigate the multifractal and asymmetric cross-correlation features between blockchain ETFs and the cryptocurrency and Nasdaq markets. Multifractality exists in the cross-correlations between blockchain ETFs and the cryptocurrency and Nasdaq markets. There is a higher persistence in the cross-correlation behaviours between blockchain ETFs and cryptocurrencies in the uptrend, whereas the persistence between blockchain ETFs and Nasdaq is more pronounced in the downtrend. On one hand, this suggests that large fluctuations in the cryptocurrency markets lead to large fluctuations in blockchain ETF markets. On the other hand, large fluctuations in the Nasdaq index lead to small fluctuations in blockchain ETF markets. These results reflect stronger ties between blockchain ETFs and cryptocurrencies compared to blockchain ETFs and the Nasdaq index. Such heterogeneous behaviour in the cross-correlation structure provides insights for short-term investment, hedging strategies, and market efficiency. Further analysis shows that long-range cross-correlation and fat-tail distributions are sources of multifractality. • Find multifractality in the cross-correlations between blockchain ETFs and the cryptocurrency and Nasdaq markets. • Show higher persistence in the cross-correlations between blockchain ETFs and cryptocurrencies during uptrend. • The persistence between blockchain ETFs and Nasdaq index is more pronounced during downtrend. • Large fluctuations in cryptocurrencies (Nasdaq index) lead to large (small) fluctuations in blockchain ETFs. • Long-range cross-correlation and fat-tail distributions are sources of multifractality. [ABSTRACT FROM AUTHOR]

The extreme price swings and complexity in cryptocurrency markets drives multifarious research into co-movements, in both time and frequency, among cryptocurrencies. In this paper, we investigate the dynamics of multiscale interdependencies among five leading and liquid cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash) using wavelet-based analyses that account for the heterogeneous behaviour of crypto-traders and crypto-investors. The results provide evidence of high levels of dependency from 2016 to 2018 at daily frequency scales. The cross wavelet transforms demonstrate Ripple and Ethereum to be trivial origins of market contagion. The results of wavelet coherence confirm the short-run and long-run market integration among some cryptocurrency pairs. However, the coherence is found to fluctuate at higher frequencies and be significantly stable at lower frequencies. Furthermore, the switch in the lead and lag relations of cryptocurrency returns suggests alternating time and frequency interdependencies. Our findings are useful to scale-conscious traders and multi-prospect (various investment horizon) investors and portfolio managers. • Use wavelet analyses to study interdependencies among cryptocurrencies. • Find high levels of dependencies during 2016 to 2018 at high frequency scales. • Ripple and Ethereum are trivial origins of market contagions. • Wavelet coherence analysis shows short and long run market integration. • Wavelet coherence fluctuates at high frequencies and is stable at low frequencies. [ABSTRACT FROM AUTHOR]