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This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers. [ABSTRACT FROM AUTHOR]

The paper investigates long memory, structural breaks, and spurious long memory in the daily trading volume of the largest and most active cryptocurrencies and stablecoins, namely, Bitcoin, Ethereum, Tether, USD coin, Binance coin, Binance USD, Ripple, Cardano, Solana, Dogecoin and Bitcoin cash. The overall results show that both long memory and structural breaks are present in the cryptocurrencies trading volume, and the detected long memory property is not driven by structural breaks but rather true and thus not spurious. Given this, we conduct out-of-sample forecasting and indicate that the ARFIMA model, which accounts for long-range dependence, has a superior forecasting performance over the standard ARIMA model for four cryptocurrencies, namely, Binance coin, Ripple, Cardano, and Dogecoin at most forecasting horizons ahead and the shorter forecasting horizon (1-day ahead) for most cryptocurrencies under investigation. [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]

Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state. [ABSTRACT FROM AUTHOR]

This paper is motivated by Bitcoin's rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices' ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers. [ABSTRACT FROM AUTHOR]

This study uses hourly data to analyse the return and volatility transmission of oil-gold and oil-Bitcoin pairs during the pre-COVID-19 and COVID-19 periods. The results show that the return transmissions vary across the two periods for both pairs. There is a unidirectional volatility spill-over from gold to oil in the pre-COVID-19 period, and from oil to gold during the COVID-19 period. There is a significant volatility spill-over from Bitcoin to oil during the pre-COVID-19 period, whereas no evidence of volatility spill-over between oil and Bitcoin is shown during the COVID-19 period. Based on optimal weights, investors should increase their investments in, (a) gold for a portfolio of oil-gold, and (b) Bitcoin for a portfolio of oil-Bitcoin during the COVID-19 period. All hedge ratios are higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. The results of hedging effectiveness reveal that the risk-adjusted returns can be improved by constructing a portfolio of oil-gold and oil-Bitcoin during both sample periods. Further results reveal that gold is a strong safe haven and a hedge for the oil market, while Bitcoin serves as a diversifier for the oil market during the COVID-19 period. [ABSTRACT FROM AUTHOR]

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability. [ABSTRACT FROM AUTHOR]

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]

This paper investigates the potential portfolio diversification between Bitcoin, bonds, equities, and the US dollar. We make use of two approaches for constructing the portfolio. The first is the standard minimum variance approach, and the alternative is based on combining risk and return when the portfolio is constructed. The portfolio based on the minimum variance approach does not result in increasing the return per unit of risk compared to the corresponding value for the best single asset, in this case, Bitcoin. However, the portfolio based on the approach that combines risk and return in the optimization problem does show a return per unit risk higher than the corresponding value for any of the four assets. Thus, the portfolio diversification benefit with respect to these four assets, in terms of return per unit risk, exists only if the portfolio is constructed via the new approach. [ABSTRACT FROM AUTHOR]

The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage. [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]

In this paper, we first estimate the monthly realised correlation, based on daily data, between stock returns of the United States (US) and Bitcoin returns. Then, we relate the realised correlation over the period October 2011 to May 2019 with a news‐based measure of the growth of trade uncertainty of the US. Our results show that the realised correlation is negatively impacted by increases in trade uncertainty, which continues to hold under alternative robustness checks, suggesting that Bitcoin can act as a hedge relative to the conventional stock market in the wake of heightened trade policy‐related uncertainties, and provide diversification benefits for investors. [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]

• Study herding behaviour in cryptocurrencies. • Find insignificant herding in static model. • Conduct rolling window and show that herding behaviour varies over time. • Run logistic regression and find that herding occurs as uncertainty increases. This study examines the presence of herding behaviour in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behaviour, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency. [ABSTRACT FROM AUTHOR]

• Study price explosivity in leading cryptocurrencies. • Apply the GSADF test of Phillips et al. (2015). • Show multiple explosivity periods in all cases. • Find explosivity in one cryptocurrency leads to explosivity in another. • Show that co-explosivity is not necessarily from bigger to smaller. Most of the limited evidence on the exponential price spikes (i.e. price explosivity) in the cryptocurrency market mainly considers the case of Bitcoin, although other cryptocurrencies have gradually eroded Bitcoin's dominance. Importantly, none has been documented as to whether explosivity periods in cryptocurrencies are contemporaneously related. Accordingly, we date-stamp price explosivity in leading cryptocurrencies and reveal that all cryptocurrencies investigated herein were characterised by multiple explosivity. Then, we determine whether explosivity in one cryptocurrency can lead to explosivity in other cryptocurrencies. Results show evidence of a multidirectional co-explosivity behaviour that is not necessarily from bigger to smaller and younger markets. [ABSTRACT FROM AUTHOR]

Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors. [ABSTRACT FROM AUTHOR]

This article contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from 19 July 2010 to 31 October 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system. [ABSTRACT FROM AUTHOR]

We examine whether Bitcoin can hedge global uncertainty, measured by the first principal component of the VIXs of 14 developed and developing equity markets. After decomposing Bitcoin returns into various frequencies, i.e., investment horizons, and given evidence of heavy-tails, we employ quantile regression. We reveal that Bitcoin does act as a hedge against uncertainty: it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns. Further, we use quantile-on-quantile regression and identify that hedging is observed at shorter investment horizons, and at both lower and upper ends of Bitcoin returns and global uncertainty. [ABSTRACT FROM AUTHOR]

We study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements. [ABSTRACT FROM AUTHOR]

This paper uses a dynamic conditional correlation model to examine whether Bitcoin can act as a hedge and safe haven for major world stock indices, bonds, oil, gold, the general commodity index and the US dollar index. Daily and weekly data span from July 2011 to December 2015. Overall, the empirical results indicate that Bitcoin is a poor hedge and is suitable for diversification purposes only. However, Bitcoin can only serve as a strong safe haven against weekly extreme down movements in Asian stocks. We also show that Bitcoin hedging and safe haven properties vary between horizons. [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]

This paper examines the Granger causality from Twitter-based economic uncertainty (TEU) to three safe-haven assets – Bitcoin, gold, and US10 year Treasury notes. Using daily data (June 1, 2011–August 30, 2021) and causality-in-quantiles and wavelets methods, the results indicate variability in the causality between the mean and variance, as well as the market conditions. TEU Granger-causes the returns and volatility of Treasuries, the volatility but not returns of Bitcoin, and neither the volatility nor the returns of gold for the raw series, and the causality is mostly significant at low and middle quantiles for Bitcoin and Treasuries. We include other risk factors and confirm the variability in the causality. Considering the possibility of a hidden causality over various frequency domains due to investors' heterogeneous expectations and perceptions of risk, the wavelet transforms-based causality tests reveal an increase in the predictability of risk indicators under specific investment horizons and market conditions. During the pandemic, TEU strongly predicts future volatility of Treasury and Bitcoin returns, reflecting the importance of social-media posts for safe-haven pricing. These findings highlight the benefits of applying the causality-in-quantiles test to decomposed series to determine the contribution of each scale to the causality over various market conditions. • Study causality from Twitter-based economic uncertainty (TEU) to three safe-havens. • Use daily data from June to August and causality-in-quantiles and wavelets methods. • Show variability in the causality between the mean/variance and market conditions. • Causality is mostly significant at low/middle quantiles for Bitcoin and US Treasuries. • Results reflect the importance of social-media posts for safe-haven pricing. [ABSTRACT FROM AUTHOR]

• Detect episodes of price explosivity and collapse in Bitcoin and Dogecoin. • Use four-hourly data and show multiple bubble episodes. • Collapse episodes are only observed in Bitcoin. • Relate price explosivity to Elon Musk's tweets. • Tweets contribute to the price explosivity of Bitcoin and Dogecoin. We detect episodes of price explosivity and collapse in Bitcoin and its contender Dogecoin using four-hourly data. The results show multiple bubble episodes in both cryptocurrencies, with a more frequent occurrence in Bitcoin. Collapse episodes are only observed in Bitcoin. We relate price explosivity to Elon Musk's tweets. His cryptocurrency-related general tweets have contributed to the price explosivity of Bitcoin, whereas his Dogecoin-specific tweets have contributed to price explosivity in Dogecoin. Our findings highlight the influential role of key persons through social media on the formation of bubbles, which matters to the decision-making of cryptotraders and market efficiency. [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]

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]

• Examine market integration among 12 leading cryptocurrencies • Apply dynamic equicorrelation (DECO) model • Show that the average return equicorrelation varies with time • Find market integration is a continuing and persistent phenomenon • Show that volume and uncertainties are main determinants of integration We examine market integration among 12 leading cryptocurrencies from August 8, 2015 to February 28, 2019. Using the dynamic equicorrelation (DECO) model, we report evidence that the average return equicorrelation is very time-varying. After experiencing large instability in 2016-2017, it increased from late 2017 till early 2019 and remained at relatively high levels. This finding points to a heightened integration in the cryptocurrency market, despite the sharp price correction in the cryptocurrency market during 2018, suggesting that market integration is a continuing and persistent phenomenon. Further examination shows that trading volume and measures of uncertainties are main determinants of integration. [ABSTRACT FROM AUTHOR]

• Study the predictability of Bitcoin returns using measures of economic uncertainty. • Newspaper- and internet search-based measures of economic uncertainty are used. • Based on monthly data, Bitcoin is a hedge against both measures. • Predictive ability of the internet-based measure is statistically stronger. • This result is confirmed by various additional analyses. We compare the ability of a newspaper-based measure and an internet search-based measure of uncertainty in predicting Bitcoin returns. Based on monthly data, we show that Bitcoin is a hedge against both measures. However, the predictive ability of the internet-based economic uncertainty related queries index is statistically stronger than the measure of uncertainty derived from newspapers in predicting Bitcoin returns, which is possibly due to the fact that the former measure of uncertainty is directly obtained by the individual investors, based on their search of the internet for terms related to uncertainty. This result is confirmed by various additional analyses. [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]

• Study linkages among the volatility "surprise" of leading cryptocurrencies. • Apply the frequency-domain test of Bodart and Candelon (2009). Bodart and Candelon (2009) • Causalities differ between the short and long runs for some cryptocurrencies. • Permanent shocks are more important over short-horizons. • Transitory shocks dominate across smaller cryptocurrencies over long-horizons. There is scarce literature examining the volatility linkages among leading cryptocurrencies, and none exists on the linkages among unexpected volatility, called 'volatility surprise'. To address this literature gap, we build on the concept of volatility surprise and examine the causal linkages among the volatility of leading cryptocurrencies via the frequency-domain test of Bodart and Candelon (2009), discriminating between transitory and permanent causalities. Permanent shocks are more important in explaining the Granger-causality that does not necessarily emanate from the largest, Bitcoin, over short horizons, whereas transitory shocks dominate the causality across smaller cryptocurrencies over long horizons. [ABSTRACT FROM AUTHOR]

• Diversification ability of Bitcoin and other leading cryptocurrencies is studied. • Bitcoin, Ethereum and Litecoin are hedges, especially in Asia Pacific and Japan. • Evidence of a time-varying diversification ability is shown in some cases. • Portfolio analysis points to diversification advantages for the hedged portfolios. • Hedging equities with cryptocurrencies is beneficial in out-of-sample analysis. Unlike most of prior studies that consider the diversification ability of Bitcoin only, we consider other leading cryptocurrencies and study the time-varying diversification ability against equities and the portfolio implications. The analyses are based on daily data. Results suggest that Bitcoin, Ethereum, and Litecoin are hedges, especially against Asian Pacific and Japanese equities, with evidence of a time-variability in some cases. Further portfolio analyses indicate the benefit of hedging equities with cryptocurrencies in both in-sample and out-of-sample analyses. Specifically, the hedging effectiveness results point to diversification advantages for the hedged portfolios compared to unhedged (equity only) portfolios. [ABSTRACT FROM AUTHOR]

• We studied price–volume multifractal cross-correlations of Bitcoin series; • We used multifractal detrended cross-correlations analysis method (MF-DCCA) and multifractal detrended fluctuation analysis (MF-DFA) to analyze the series; • We used a new cross-correlation test proposed by Podobnik et al. to measure qualitatively cross-correlation; • We found existence of price–volume multifractal cross-correlations; • We also found that the level of cross-correlations is very strong by computing DCCA coefficient. We study the price–volume cross-correlation in the Bitcoin market from July 17, 2010, to May 2, 2018, via the multifractal detrended cross-correlations analysis (MF-DCCA). Results show that Bitcoin prices changes and changes in trading volume mutually interact in a nonlinear way. Furthermore, multifractality is present and significant. By bringing fractal market and nonlinear theories into the analysis of Bitcoin price-volume behavior, we characterize the underlying mechanisms (i.e., nonlinear dependency and multifractality) that govern Bitcoin market dynamics. This deepens our insights into the effectiveness of technical trading strategies in the complex market of Bitcoin that seems to lack efficiency. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between clean energy stock indices and energy metals that are sensitive to the growth in demand for clean energy solutions, and makes inferences about whether energy metals can act as hedges or safe havens for clean energy stock indices. The sample period is April 2011 to April 2021, a period which saw substantial investments into the clean energy industry as the capital deployed to clean energy generation during this period was about three times higher than the preceding decade. The main results indicate statistically significant non-linear relationships among the markets under study. All energy metals, except cobalt, have a significant positive linkage with clean energy stock indices and such associations do hold during episodes of high volatility. While none of the energy metals under study acts as a hedge for clean energy stock markets, the results support previous evidence on the hedging properties of precious metals, showing that gold and silver serve as hedges for certain clean energy stock indices. These results have important implications amid tremendous growth in clean energy stock investments and the repeating occurrence of periods of uncertainty. • We investigate if energy metals can hedge clean energy equity risk. • None of the energy metals acts as a hedge. • Gold and silver act as hedges for certain clean energy stocks. [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]