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Herding Behavior and Liquidity in the Cryptocurrency Market
Abstract
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.
... In the time mentioned above, cryptocurrencies have emerged as a burgeoning digital financial asset, garnering significant attention from investors, portfolio managers, regulators, and researchers. It has seen explosive growth due to the increasing attention it has garnered (Arsi et al., 2021;Cao and Xie, 2022). As of 30th September 2023, the number of cryptocurrencies in existence exceeds 1.8 million, collectively having a market capitalisation of $1,080 billion. ...
... The pandemic in 2020 and the war in 2022 induce anxiety among investors and foster collective decision-making through herding behaviour. Herding behaviour refers to a phenomenon in which investors deviate from using their own investing strategy and instead choose to mimic the actions of others while making investment decisions (Bikhchandani and Sharma, 2000;Arsi et al., 2021). Prior research, such as by Kumar et al. (2022), Bouri et al. (2021), Jana et al. (2023) and Jana and Sahu (2023c), have revealed similar results. ...
Purpose
This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.
Design/methodology/approach
This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.
Findings
The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.
Originality/value
This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.
... The author found a presence of herding behavior during up markets across the entire dataset. Arsi et al. (2022) point out that herding behavior in ten leading cryptocurrencies from 2016 to 2019 was influenced by the state of market liquidity. Herding asymmetries are not only perceived during bull and bear phases, but also on days with high and low liquidity. ...
... Based on the estimation results obtained from the CSAD model, it is inferred that the R 2 m,t is not significantly negative, thereby indicating the absence of herding behavior in the cryptocurrency market. This conclusion is in consonance with prior studies by Arsi et al. (2022), Bouri et al. (2019), and Jia et al. (2022). Since the CSAD model is considered more appropriate for studying the herd effect, it is used in the remainder of this study. ...
This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.
... Such effects are not always concentrated in highly liquid currencies, creating a framework for connectedness and spillover in liquidity shocks. Arsi et al. (2021) conclude that herding connects liquidity across cryptocurrencies. While the existing literature provides insight into the dynamics of within-market liquidity spillovers and the identity of net transmitters and receivers, there has been no attempt to study these markets jointly. ...
... Naturally, the cryptocurrency and FX markets 10 are no exception. During turbulent periods, herd behaviour intensifies as trading strategies become correlated as traders mimic each other (Arsi et al., 2021). Correlated trades across markets during times of stress thus promote liquidity spillovers. ...
... Research is consistent with the assumption that market asymmetries aggravate anxiety, resulting in ''group-thinking'' and herd (Yousaf & Ali, 2020). Most studies examine external market movements-heterogeneous returns, trading volumes, volatility, liquidity, and periods of crisis as external drivers of the herd (Arsi et al., 2022). The past literature has examined herding during the Asian financial crisis (Chiang & Zheng, 2010), the Eurozone crisis (Mobarek et al., 2014), and the subprime crisis (Andrikopoulos et al., 2017) and COVID-19 (Bharti & Kumar, 2022b;Espinosa-Me´ndez & Arias, 2021). ...
This study is anchored to assimilate the body of knowledge on herd behavior in financial markets to understand the evolution of the subject, focal concepts, and core areas researched in the past, as well as present the agenda for future research. The data involved carefully selecting 214 research articles published from 1994 to 2023 in journals indexed in the Web of Science (WoS) database using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The paper examines the evolution and growth of the subject. It performs thematic review of the top 100 most influential articles to identify six focal concepts and areas discussed in the past. The findings are synthesized into three primary themes to develop a comprehensive intellectual structure for the future. The results reveal that most studies examine equity market herding in developed countries. Additionally, theoretical and empirical studies are disconnected, resulting in a partial understanding of the determinants of herd behavior. Further, there is a dominance of empirical-based studies that are inadequate in explaining the complex herd behavior. This study makes significant academic contributions through thematic review by identifying the research gaps and developing a structured framework for future research agenda complemented with the proposed methodological approach that can be used as a reference for forthcoming research.
... A cryptocurrency-based financial service known as decentralised finance (DeFi) [21]. It is an open system of finance in which smart contracts are used to build exchanges as well as provide a wide range of services, such as insurance, yield farming, and lending, without relying on central authority [6]. ...
... Once shareholders share their confirmation, stored data cannot be changed. Stakeholders involved in a transaction act as nodes, and validation is performed through cryptography (Awad et al. 2018;Ammous 2018;Arsi et al. 2021;Bouri et al. 2021a;2021b;2022b). Blockchain is the technology at the base of the cryptocurrency existence: it was created to support digital currencies without assisting in predicting the return of the latter (Shahzad et al. 2022b;Wang et al. 2022;Wen et al. 2022), nor guiding customers' individual trading strategies (Shahzad et al. 2021a;. ...
... Once shareholders share their confirmation, stored data cannot be changed. Stakeholders involved in a transaction act as nodes, and validation is performed through cryptography (Awad et al., 2018;Ammous, 2018;Arsi et al., 2021;Bouri et al., 2021a;. Blockchain is the technology at the base of the cryptocurrency existence: it was created to support digital currencies without assisting in predicting the return of the latter (Shahzad et al., 2022b;Wang et al., 2022;Wen et al., 2022), nor guiding customers' individual trading strategies (Shahzad et al., 2021a;. ...
Grounded in Transformative Service Research (TSR), this study aims to examine the mechanism by which blockchain-enabled banking determines customers perceived financial well-being (FW). We conclude that blockchain features augment information transparency, which in turn determines customers perceived financial well-being. Data was collected through a survey filled by 283 individuals having bank accounts. The contextual setting of the study was provided by commercial banks operating in Pakistan. The primary data was analyzed through PLS-SEM to explore the direct and indirect relationships among blockchain features (efficiency, security, and regulatory compliance), perceived information transparency, and perceived financial well-being. The evidence points to the fact that: (1) Blockchain features are significant in determining customers’ financial well-being; (2) Information transparency mediates the relationship between these features and customers perceived financial well-being; hence, that (3) Bank managers who embrace the challenging task of improving the perceived financial well-being of their customers, should adopt blockchain technology to enhance information transparency, and accordingly to augment the customers’ financial well-being.KeywordsBlockchainfirm information transparencyperceived financial well-being
... Once shareholders share their confirmation, stored data cannot be changed. Stakeholders involved in a transaction act as nodes, and validation is performed through cryptography (Awad et al. 2018;Ammous 2018;Arsi et al. 2021;Bouri et al. 2021a;2021b;2022b). Blockchain is the technology at the base of the cryptocurrency existence: it was created to support digital currencies without assisting in predicting the return of the latter (Shahzad et al. 2022b;Wang et al. 2022;Wen et al. 2022), nor guiding customers' individual trading strategies (Shahzad et al. 2021a;. ...
This study aims to identify the determinants of purchasing private label brands (PLBs) by non-buyers and occasional buyers. This study aims to identify levers that may raise the purchasing rate of non and occasional Private Label Brands (PLBs) buyers.We conducted online quantitative research with 151 customers from a panel attending one of the French retail chains. In addition, we used PLS-SEM to test the research model. The findings show that attitude toward PLBs plays a central role in converting non and occasional PLBs buyers into more regular buyers. Furthermore, theoretical contributions and managerial implications are developed.KeywordsPrivate Label BrandAttitudeTrustOccasional Buyers
... Notably, the academic literature on the financial and economic aspects of NFT is at a nascent stage, but rapidly evolving, in contrast to studies of mature conventional markets. An early study by Bao and Roubaud (2021) emphasizes the latest developments and trends in Fintech, summarizing the extensive literature on cryptocurrencies, NFTs and other related markets. Nadini et al. (2021) discuss the overall structure and evolution of the NFT market by mapping the NTF ecosystem based on sales and traded volumes across projects. ...
Stylized facts of returns and volatility are an important approximation tool for empirical finance studies, especially in the area of young and new assets. In this paper, we examine the return and volatility properties of four non-fungible tokens (NFTs) and four cryptocurrencies from 24th January 2018–2nd August 2022. The results show the following: Firstly, the returns of both NFTs and cryptocurrencies have fat tails, with evidence of tail exponents following the inverse cubic law, along with clear persistence behavior. Secondly, all returns exhibit volatility clustering, albeit to varying degrees, and the detected absence of inverse volatility-asymmetry challenges the safe-haven property often documented for cryptocurrencies. Thirdly, return-based long-memory is slightly more intense than volatility-based long-memory, especially for NFTs, which demonstrate a predictability contesting market efficiency. These findings are generally consistent with previous findings on equities, implying that the return and volatility behavior of NFTs and cryptocurrencies is leaning towards that of traditional assets.
... For example, Kristoufek and Vosvrda 20 find that most cryptocurrencies are inefficient in some periods, Chu et al. 21 study Bitcoin and Ethereum and find that efficiency of these assets vary over time, Arsi et al. 22 find evidence of herding behavior in the cryptocurrency market, whereas Palamalai et al. 23 study 10 cryptocurrencies and find evidence that they do not follow a random walk, implying that they are weak-form inefficient. ...
Cryptocurrencies play a pivotal role in the financial market. Given this, we perform the asymmetric multifractal cross-correlation analysis to examine the weak form of the Efficient Market Hypotheses (EMH) considering two temporal scales. In the daily scale, we find that the pair Bitcoin–Litecoin displays the largest multifractal spectrum. While, in the hourly scale, the pair Bitcoin–Ethereum presents the largest multifractal spectrum. Our empirical evidence has rejected the weak form of the EMH and clearly suggests that the dynamics of the analyzed cryptocurrency pairs are in line with the Fractal Market Hypothesis (FMH). Cross-correlation asymmetries are more persistent for small fluctuations than for large fluctuations. The results are essential for investors, portfolio and risk managers, and policymakers.
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.
This study employs state‐space models and the quantile‐on‐quantile regression technique to examine the dynamics of intentional and fundamental herding in North American energy stocks, together with its non‐linear determinants. Our findings reveal persistent herding in North American energy stocks, with no exceptions during the global financial crisis, COVID‐19, lockdown and post‐lockdown period, which is primarily driven by intentions. In all of these cases, herding is primarily motivated by intentional drivers rather than fundamental factors. Our results also show a herding asymmetry during bullish and bearish market conditions, but herding during bullish (bearish) market condition is mainly driven by the intentions (fundamentals). The findings of our quantile‐on‐quantile regression show that the effects of macroeconomic variables on time‐varying herding are, in fact, regime‐specific. We generally find evidence of herding (anti‐herding) at the lower (upper) quartiles of energy stocks liquidity, oil price shocks, economic policy uncertainty and oil market implied volatility. The investment community engaged in North American energy stocks can benefit from our study, as awareness about regime‐specific effects of various market variables on intentional (fundamental) herding can be a useful input in designing asset allocation and hedging strategies. The outcome of our study may provide interesting input to regulators to develop the dynamic legislative framework focusing on policies to encourage investment diversification during regime‐specific systemic risk escalation resulting from the dynamic behaviour of herding and macroeconomic variables.
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.
Purpose
This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect.
Design/methodology/approach
The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect.
Findings
The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect.
Practical implications
These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty.
Originality/value
Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.
In recent years cryptocurrency trading has been very active and received attention from worldwide investors. Given its short history and lack of effective regulatory frameworks, cryptocurrency trading is still like the wild west. This study selected the top 21 dominant cryptocurrencies to analyze the
trading behavior associated with them over the period from 2016 to May 2023. We adopted the cross-sectional absolute deviations (CSAD) model to investigate investors’ trading behavior. Two
calculation methods are used: the capitalization-weighted method and the equally weighted method. Our analysis results have indicated that no matter which calculation method is used, the
cryptocurrency marketplace has shown strong and significant herd behavior from 2016 to March 12, 2020. However, significant herd behavior couldn’t be detected after March 12, 2020. This is a very interesting and yet important finding from our study. The implication is that March 12, 2020 appeared to be a turning point where important market conditions might have changed, such as the dominant cryptocurrency trading force has transformed from individuals to institutional investors.
This study employs information economics and the financial intermediary theory to explore the influences of private information in virtual communities and financial technology (fintech) derived from virtual currency on financial stocks. The paper conducts robust analyses on 67,166 data observations of the stock markets in China and Taiwan and finds that virtual currency development causes a structural change in the financial industry. The financial stocks in Taiwan are obviously driven by virtual factors, whereas those in China are subject to both pull from substantial factors and push from virtual factors. The research findings also suggest that the non-fundamental herding behavior driven by private information interferes with the value of financial stocks. However, financial innovations boost the competitiveness of the financial industry. It is advised to establish a policy to closely monitor the diffusion of private information and the exchange rate volatility between cryptocurrencies and home currencies to facilitate proactive financial risk management.
Cryptocurrencies have been historically characterised by large price swings and inherent volatility at a much higher scale than traditional financial assets. Understanding the underlying mechanisms and whether, or how, these are priced in through possible risk premia is crucial to bringing cryptocurrencies closer to mainstream financial markets. Using data on 1982 cryptocurrencies form January 1, 2015 till September 30, 2020 and a combination of models involving portfolio-level and Fama–MacBeth analyses, while accounting for cryptocurrency sample selection, we show that the additional risk measured by idiosyncratic volatility is well priced in cryptocurrencies and investors are being paid a risk premium for their holdings. However, a deeper inspection of the dynamics reveals that such a trade-off is mostly valid for the most illiquid cryptocurrencies, which are susceptible to microstructure noise.
This paper examines the extent to which herding and feedback trading behaviors drive price dynamics across nine major cryptocurrencies. Using sample price data from bitcoin, ethereum, XRP, bitcoin cash, EOS, litecoin, stellar, cardano and IOTA, respectively, we document heterogeneity in the types of feedback trading strategies investors utilize across markets. Whereas some cryptocurrency markets show evidence of herding, or, ‘trend chasing’, behaviors, in other markets we show evidence of contrarian-type behaviors. These findings are important because they elucidate upon, firstly, what forces drive cryptocurrency markets and, secondly, how this type of trading behavior affects autocorrelation patters for cryptocurrencies. Finally, and from our intertemporal asset pricing model, we shed new light on the observed nature of the risk-return tradeoffs for each of our sampled cryptocurrencies.
This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
This paper investigates herding behavior and the connection between herding behavior and investor sentiment. We apply a Cross-Sectional Absolute Deviation (CSAD) approach and the quantile regression method to capture herding behavior in the KOSPI and KOSDAQ stock markets. The analysis results are outlined as follows. First, we find that herding behavior is exhibited during down-market periods in the KOSPI and KOSDAQ stock markets. However, we show that adverse herding behavior occurs in low-trading volume and low-volatility periods. Second, according to the results of the quantile regression, herding behavior is found in the low and high quantiles of the KOSPI and KOSDAQ stock markets. However, adverse herding behavior is also found, which means that investors herd in extreme market conditions. Third, the relationship between investor sentiment and herding behavior is analyzed through regression and quantile regression, and investor sentiment is confirmed to be one of the important factors that can cause herding behavior in the Korean stock market.
This paper investigates the existence of herding behavior in cryptocurrencies market. Using data of the 20 large cryptocurrencies and MV Index Solution Crypto Compare Digital Assets for large cap index, we found no evidence of herding behavior using cross-sectional absolute standard deviation estimation. However, by applying a rolling window analysis, the results show significant herding behavior, which varies over time. Finally, we find an inverse relationship between herding behavior and the Bloomberg consumer comfort index which means that when traders are less comfortable they prefer to ignore their expectations and follow the market performance.
This paper sets out to explore whether the investor herding in the cryptocurrency market induces correlations in cryptocurrency returns using the methodology of Chang, et al, (2000) and Galariotis, et al. (2015) from a daily data sampling period of 3/30/2015 to 5/24/2019. The initial regression results show that the cross-sectional absolute standard deviation of return can only be explained by GSCI oil and gold index return but no relationship exists between cross-sectional absolute standard deviation of return and other regression variables such as return on CCi30, US equity risk premium and US/Euro exchange rate return. The herding regression results under normal market condition show that there exists a strong tendency to herd on non-fundamental information that explains cross-sectional absolute standard deviation of returns. As such, cryptocurrency returns cannot be predicted on the basis of fundamental economic information (e.g., major macroeconomic announcements). Herding on non-fundamental information is found to be more pronounced during upward-trending period of the market and other than upward-trending period. No signs of herding on fundamental information could be observed under other market conditions. Although the theory suggests that herding on non-fundamental information results in efficient outcome, above findings do not encourage diversification of traditional assets with cryptocurrency on the basis of low correlation. Since cryptocurrency lacks intrinsic value, the exchange is shown to provide a pseudo efficient trading platform for speculative investors. Implications for future research are discussed.
According to the social learning literature, agents faced with asymmetries rationally ignore their own information to follow the herd. We argue that investors in listed SMEs that are known for their high informational opacity exhibit more herding compared to investors in large firms in order to lower the impact of informational asymmetries. Given a gap in the listed SMEs’ literature, we test our hypothesis offering first time evidence on their herding behavior. The results support our hypothesis. Herding is more prevalent among listed SMEs (known as micro-caps in the US) during normal periods, while during crisis periods, all micro-cap investors are faced with the same informational problem and do not herd because they expect that none of them has information of better quality. In addition, we reveal cross-market herding effects and a positive link of liquidity and herding. The results have implications for portfolio management and regulatory authorities among others.
This paper investigates the level of liquidity of digital currencies during the very intense bearish phase in their markets. The data employed span the period from April 2018 until January 2019, which is the second phase of bearish times with almost constant decreases. The Amihud’s illiquidity ratio is employed in order to measure the liquidity of these digital assets. Findings indicate that the most popular cryptocurrencies exhibit higher levels of liquidity during stressed periods. Thereby, it is revealed that investors’ preferences for trading during highly risky times are favorable for well-known virtual currencies in the detriment of less-known ones. This enhances findings of relevant literature about strong and persistent positive or negative herding behavior of investors based on Bitcoin, Ethereum and highly-capitalized cryptocurrencies in general. Notably though, a tendency towards investing in the TrueUSD stablecoin has also emerged.
This paper examines the leverage effect and the information flow interpretation of heteroskedasticity-from a sample of daily Bitcoin return data from 3/19/2016 to 7/24/2018-using the framework of Lamoureux and Lastrapes (1990). The results show that the Bitcoin return variance cannot be effectively explained by GARCH (1, 1), GJR-GARCH or EGARCH models given the stationarity of variance of return. The leverage effect is not observed by the estimate of EGARCH model. ARCH effect vanishes and the coefficient becomes highly statistically insignificant when the volume-as a mixing variable-is included in the conditional variance equation of IGARCH model. These findings suggest that the Bitcoin price changes are generated from an independent stochastic price increment process of which the increments are subordinated to stationary ARCH errors. As such, the Bitcoin can be classified as a class of speculative assets in the cryptocurrency exchange.
Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.
This paper investigates whether Bitcoin acts as a diversifier, hedge or safe haven tool for investors in major developed and developing markets, as well as for commodities. This paper employs the GARCH Dynamic Conditional Correlation (DCC) model. The sample covers seven developed and six developing countries, five regional indices and 10 commodity series. The results show that Bitcoin acts as a hedge for investors in most of the developing countries such as Brazil, Russia, India and South Korea, but only as a diversifier for investors in developed countries and for commodities. Moreover, Bitcoin acts as a diversifier for all the 10 commodities studied here. During the US election in 2016, Brexit referendum in 2016, and the burst of Chinese market bubble in 2015, Bitcoin acted as a safe haven asset for both the US and non-US investors. Understanding the role of Bitcoin is important for financial market participants who seek protection against market turmoil and downward movements. Furthermore, our findings would be of interests to regulators and governments to engage in more discussion of the role of Bitcoin in financial markets. This paper contributes to the ongoing debate on the usefulness of Bitcoin for investments. Furthermore, it distinguishes the benefits of Bitcoin as a diversifier, hedge and safe haven to investors in the developed versus developing markets.
In this paper, we use vine copula approaches to model the co-dependence and portfolio value-at-risk (VaR) of six cryptocurrencies using data of daily periodicity from September 2015 to June 2018. We establish evidence of strong dependencies among the virtual currencies with a dynamic dependency structure. We find that among the class of cryptocurrencies examined, Ethereum offers the best optimal and economically risk-reward trade-off subject to a no-shorting constraint for portfolio investors using the efficient frontier. Given the paucity of empirical research on the cryptocurrency markets, this paper provides new insights, which could be useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets.
In this study, we employ a TVP-FAVAR connectedness approach in order to investigate the transmission mechanism in the cryptocurrency market. To this end, we concentrate on the top 9 cryptocurrencies by virtue of market capitalization and one market factor { based on 45 additional digital currencies { capturing the co-movements in the cryptocurrency market. The period of study spans between August 7, 2015 and May 31, 2018. We find that the dynamic total connectedness across several cryptocurrencies exhibits large dynamic variability ranging between 25% and 75%. In particular, periods of high (low) market uncertainty correspond to strong (weak) connectedness. We show that these results could be explained on the basis of increased market uncertainty that is associated with periods of highly volatile prices. In addition, despite the fact that Bitcoin still in uences the cryptcurrency market substantially, we note that, recently, Ethereum has become the number one net transmitting cryptocurrency. We further note that the market gradually becomes more complex considering our connectedness approach and that this might be attributed to the unique characteristics and possibilities inherent in the technology of each cryptocurrency. A simplified application concentrating on bivariate portfolios is indicative of potential hedging opportunities using dynamic hedge ratios and dynamic portfolio weights.
We are interested in describing the returns and volatility dynamics of major cryptocurrencies. Very high volatility, large abrupt price swings, and apparent long memory in volatility are documented features of such assets. We estimate a multivariate stochastic volatility model with discontinuous jumps to mean returns and volatility. This formulation allows us to extract a time-varying shared average volatility and to account for possible large outliers. Nine cryptocurrencies with roughly three years of daily price observations are considered in the sample. Our results point to two high volatility periods in 2017 and early 2018. Qualitatively, the permanent volatility component seems driven by major market developments, as well as the level of popular interest in cryptocurrencies. Transitory mean jumps become larger and more frequent starting from early 2017, further suggesting shifts in cryp-tocurrencies return dynamics. Calibrated simulation exercises suggest the long memory dependence features of cryptocurrencies are well reproduced by stationary models with jump components.
We test for herding in crypto-currency markets using the CSAD method of Chang et al. (2000). Daily returns of 6 major crypto-currencies and market index CCI30 for the period 07-08-2015 t0 18-01-2018 is used. Possibility of herding under up and down market and high and low volatility is tested. Herding is found under up and down market activity, indicating over-enthusiasm and over-reaction. Market volatility is found not to have any significant impact on herding behavior. Herding is found to be dependent upon the market activity rather than market volatility.
This paper establishes a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by a social media based investor happiness index built on Twitter feed data. Our analysis of daily data for nine developed market currencies suggests that the foreign exchange market is generally characterized by strong anti-herding behavior. Utilizing the quantile-on-quantile (QQ) approach, developed by Sim and Zhou (2015 Sim, N., and H. Zhou. 2015. “Oil Prices, US Stock Return, and the Dependence between Their Quantiles.” Journal of Banking & Finance 55:1–8. doi:10.1016/j.jbankfin.2015.01.013[Crossref], [Web of Science ®] , [Google Scholar]), we show that the relationship between investor sentiment and anti-herding is in fact regime specific, with anti-herding behavior particularly prominent during states of extreme investor sentiment. The effect of sentiment on anti-herding is generally stronger in extreme bullish sentiment states, while average sentiment is associated with less severe anti-herding. The findings lend support to the behavioral factors for asset pricing models and suggest that real time investor sentiment signals can be utilized to monitor potential speculative activities in the currency market.
This paper sets out to explore whether convergence and herding phenomena exist for digital currencies. Daily data cover a large spectrum of cryptocurrencies in separate bull and bear periods. Empirical estimations for detecting club convergence and clustering are performed by the methodology proposed by Phillips and Shu (2007, 2009). Econometric outcomes reveal preliminary evidence of powerful herding behaviour. The lowest of large-cap digital currencies attract mainly cryptocurrencies which are about in the middle of the large-cap and medium-cap categories whereas the highest-cap cryptocurrencies are parts of convergence clubs with mostly large-cap or purely medium-cap digital currencies during bear markets. Notably, segmentation is higher during bear markets as clusters are formed around more numerous cryptocurrencies than during bull markets. Convergence is stronger during flourishing periods.. Secondary herding is also realized among pairs of clubs. Our findings enable investors to better diversify their portfolios and ameliorate their risk-return trade-off during extreme events.
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.
Herding is a feature of investor behavior in financial markets, particularly in market stress. We apply an approach based on the cross-sectional dispersion of individual stocks' betas, which allows us to extract herding patterns, using two dynamic methodologies to measure the herding phenomenon over time with a state-space model for the Cryptocurrency Market. The results reveal that herding toward the market shows significant movement, and persistence regardless of the market condition, expressed through the market index, market volatility, and the volatility index. When analyzing path herding is possible to observe that herding was intense during the investigated period. We also identify a positive relationship between herding and market stress.
The enormous rise of the cryptocurrencies over the last few years has created one of the largest unregulated markets in the world. In this study, we obtain millisecond data for the five major cryptocurrencies—bitcoin, ethereum, ripple, litecoin and dash—and two cryptocurrency indices—Crypto Index (CRIX) and CCI30 Crypto Currencies Index—to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during periods of extreme price movements (EPMs). We demonstrate that cryptocurrency traders (CTs) facilitate EPMs and demand liquidity even during the utmost EPMs. We observe the presence of herding behaviour during up markets across the entire dataset. Our robustness checks indicate that herding behaviour follows a dynamic pattern that varies over time with decreasing magnitude. We also provide novel evidence of CTs’ profitability after transaction costs, and demonstrate their strong profitability-generating record in the future.
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.
The aims of this paper are to detect evidence of institutional investor herding behaviour and examine the role that investor sentiment plays in institutional investor herding behaviour. The herding behaviour is investigated by examining the dispersion of time varying beta of UK open-end and closed-end funds. The study finds evidence of fund manager herding behaviour, which suggests they are likely to herd on market portfolio, size, and value factors. UK market-wide investor sentiment index is used for investigating the effects of investor sentiment on institutional herding behaviour. We find a unidirectional investor sentiment effect on the herding of UK mutual fund managers. We also reveal that the sentiment factors affecting UK open-end and closed-end fund managers herding behaviour are different due to the differences in fund structure.
Cryptocurrencies have emerged as an innovative alternative investment asset class, traded in data-rich markets by globally distributed investors. Although significant attention has been devoted to their pricing properties, to-date, academic literature on behavioral drivers remains less developed. We explore the question of how price dynamics of cryptocurrencies are influenced by the interaction between behavioral factors behind investor decisions and publicly accessible data flows. We use sentiment analysis to model the effects of public sentiment toward investment markets in general, and cryptocurrencies in particular on crypto assets’ valuations. Our results show that investor sentiment can predict the price direction of cryptocurrencies, indicating direct impact of herding and anchoring biases. We also discuss a new direction for analyzing behavioral drivers of the crypto assets based on the use of natural language AI to extract better quality data on investor sentiment.
This chapter examines the time-varying causal relationship between trading volume and returns in cryptocurrency markets. The chapter employs a novel Granger causality framework based on a recursive evolving window procedure. The procedures allow detecting changes in causal relationships among time series by considering potential conditional heteroskedasticity and structural shifts through recursive subsampling. The chapter analyzes the return-volume relationship for Bitcoin and seven other altcoins: Dash, Ethereum, Litecoin, Nem, Stellar, Monero, and Ripple. The results suggest rejecting the null hypothesis of no causality, indicating bi-directional causality between trading volume and returns for Bitcoin and the altcoins except Nem and Stellar. The findings also highlight that the causal relations in cryptocurrency markets are subject to change over time. The chapter may conclude that trading volume has predictive power on returns in cryptocurrency markets, implying potential benefits of constructing volume-based trading strategies for investors and considering trading volume information in developing pricing models to determine the fundamental value of the cryptocurrencies.
This paper examines the system of Bitcoin exchanges with respect to their common dynamics. We employ connectedness measures based on the daily realised volatility of Bitcoin prices, for which the results reveal that Coinbase is the clear leader of the market, while Binance ranks surprisingly weak. The positions of specific exchanges within the network of connectedness seem to be driven by these exchanges’ own characteristics, from which trading in USD rather than USDT (Tether) stands out. Our findings suggest that safer asset withdrawal matters more to the volatility connectedness among Bitcoin exchanges than does trading volume.
This paper investigates two behavioral biases—the disposition effect and herding—using the Mt. Gox data between 2011–2013 in the bitcoin cryptocurrency market. Using trade round-trip and survival analysis, it shows the market exhibits a reverse disposition effect in bullish periods and the usual positive disposition effect in bearish periods. It finds evidence of herding in bearish as well as bullish periods using a return dispersion model. Additionally, it shows that herding moves along the market trend. Herding increases in both bullish and bearish periods when the bitcoin price increases and decreases, respectively.
This paper provides a thorough review of the liquidity measures that are used in the empirical literature to measure liquidity. A wide range of papers have emphasized its role and the need to manage and understand this topic, which had hitherto not been deeply explored. Literature on liquidity proposes a wide set of liquidity measures and proxies intended to measure the different characteristics and dimensions that liquidity presents. Early papers analyzing the liquidity issue were based on quotation data or on end-of-month prices, given that databases with widely complete transaction information were not available. The recent availability of high frequency databases has allowed researchers not only to develop new measures but also to adapt to other markets a comprehensive set of existing measures. In this paper, we classify and describe the variety of the existing liquidity measures and proxies depending on the aspect of liquidity that one wants to address.
I investigate the relationship between the stock market liquidity and investors sentiment. The
significance of the liquidity in asset pricing is well documented, but little attention is paid in the
empirical literature to the effect of investors sentiment on variation in the liquidity. I construct
irrational aggregate sentiment index (ASI) measure the institutional investors sentiment. The empirical
findings suggest that the stock market is highly liquid when sentiment is bullish and vice versa. Using
the non-linear conditional volatility framework and non-linear Granger causality, I show the significant
role of investors sentiment in predicting the stock market liquidity. The past psychological biases
and herding of investors are associated with the volatility of liquidity through the direct and indirect
channels.
We contribute to the ongoing debate on the existence of herding behavior in the crypto market and provide statistically significant evidence thereof. This finding is in contrast to existing empirical evidence in this field, which is primarily due to previous studies suffering from a sample bias. By introducing the concept of beta herding to the debate, we provide further robustness for our results. Moreover, we propose the concept of Bitcoin as a ‘transfer currency’ and empirically show that herding measures centered around such a transfer currency provide a more precise representation of dispersion in investors beliefs on the crypto market.
The study investigates whether herding behavior is present in the rapidly emerging cryptocurrency market. By analyzing daily data from major cryptocurrencies during the period August 2015 to December 2018, we find evidence that investors in the cryptocurrency market act irrationally and imitate other's decisions with no reference to their own beliefs. Furthermore, our empirical results provide evidence that the up-events market dispersion follows market movements at a faster pace compared to the down events. Thus, cryptocurrencies show a behavior where they tend to move in tandem, which does not necessarily reflect their fundamentals.
Cryptocurrency has experienced the skyrocketing and falling back in 2018. Beyond the hype, the specific price movements of different cryptocurrencies should be investigated in a more careful way. Since the cryptocurrency market is a non-linear complex system which are not suitable analyzed by tradition methods, this paper introduces methods from econophysics. Mono-fractal analysis (detrended fluctuation analysis, DFA) is applied to investigate the price movement. Further, multi-fractal fluctuation detrended analysis (MF-DFA) is used for robustness test. Through analyzing four representative cryptocurrencies, our paper finds a strong momentum effect in BTC and ETH market, and a reversion effect in XRP and EOS when large fluctuation occurs. These findings may provide a reference for trading strategy in alternative asset allocations.
The growing cryptocurrency market has attracted the attention from many investors worldwide, mainly due to the ease of entering the market and its extremely volatile character. The main aim of this paper is to examine interdependencies between log-returns of cryptocurrencies, with the special focus on Bitcoin. Based on implications from the literature, we use methods dedicated for studying the stock market and apply the two-step analysis, comparing results between two subsequent periods. Results obtained using Minimum Spanning Tree (MST) method show that cryptocurrencies form hierarchical clusters, consistently over two separate periods, indicating potential topological properties of the cryptocurrency market. Then, using Vector Autoregression (VAR) model, we study the transmission of demand shocks within clusters. Results show that changes in Bitcoin price do not affect and are not affected by changes in prices of other cryptocurrencies. However, results indicate that findings obtained for Bitcoin shall not be generalized to the entire cryptocurrency market.
Using a dataset on local banks’ daily FX transaction volume segregated into counterparty and transaction types, this article investigates the relationship between trading volume and intraday realized volatility for the US dollar/Turkish lira parity (USDTRY), one of the most traded emerging market currencies against US dollar. We question whether type of counterparty and transaction affects intraday volume-volatility relationship across various trading sessions around the world. We reveal that only the spot transactions of domestic customers have positive contemporaneous relation with realized volatility and this significance is valid only in global trading sessions that mostly overlap with the local trading hours. Furthermore, we utilize a metric for the belief dispersion on the level of future exchange rate via currency options and find that the dispersion significantly strengthens the volume-volatility nexus, confirming the Dispersion of Beliefs Hypothesis.
Analogous to the way wind blows single grains of sand and the subsequent settling back atop sand dunes, we find statistical evidence to claim that the prices of cryptocurrencies exhibit similar unpredicted patterns, characterized by positive or negative jumps. Motivated by extant evidence of asset returns’ non-normality, we capture distributional properties of the log-returns of the Bitcoin and the following three cryptocurrencies in terms of market capitalization (Ethereum, Ripple and Bitcoin cash). The total error induced by the fitted distribution is remarkably decreased when the generalized hyperbolic distribution is used, a finding further validated by a series of goodness-of-fit type statistical tests. A complementary analysis for the foreign exchange market is conducted, with inherent similarities to that of cryptocurrencies. We reveal that the generalized hyperbolic distribution can also be used to model very widely traded currency pairs significantly more accurately than the log-normal.
In view of the growing popularity of cryptocurrencies, the purpose of the present study is to offer new insights on the herding behavior of cryptocurrencies. To this end, we employ market prices of the largest cryptocurrencies for a period extending from August of 2015 through February of 2018. Results derived from the standard testing procedure using ordinary least squares pointed to the existence of herding behavior in the cryptocurrencies’ market. Evidence on herding effects are further corroborated employing a quantile regression that accounts for the asymmetric nature of cryptocurrencies’ returns. However, herding behavior is no longer present when a more robust time-varying regression model is employed. Our results entail significant implications for researchers, investors and market authorities.
This study aimed to analyze herding behavior and contagion phenomena in the cryptocurrency market. We selected 50 of the most liquid and capitalized currencies in the period from March 2015 to November 2018 (daily data). The methodology used for detecting herding behavior comprised adaptations of the cross-sectional absolute deviation (CSAD) and cross-sectional standard deviation (CSSD) tests, as well as Hwang and Salmon's (2004) model. For the contagion effect, we utilized adaptations of Forbes and Rigobon's (2002) (FR) test, and FR test extensions based on the comoments of Fry, Martin, and Tang (2010) and Fry-McKibbin and Hsiao (2018). The results of using the CSSD test and Hwang and Salmon's (2004) state space model revealed herding behavior, demonstrating extreme periods of adverse herd behavior. As regards the contagion effect, the modified FR test and its extensions with comoments were able to identify the Bitcoin contagion in other currencies in almost all cases.
This study investigates the extent to which herding towards the market consensus for Russian stocks is driven by fundamental and non-fundamental factors. We find evidence that investors on the Moscow Exchange herd without any reference to fundamentals during unanticipated financial crises coupled with high uncertainty, in falling markets, and during days with extreme upward oil price movements. The results indicate that companies with less transparent information environment, proxied by company size and the number of analysts following the company, are more prone to herding driven by non-fundamental factors. This herding behaviour temporarily impedes the incorporation of all relevant fundamental information into stock prices and diverts the market from its efficient state. In contrast, in periods of high liquidity and on days of international sanction announcements during the Ukrainian crisis herding behaviour is merely driven by fundamentals. In Russia, macroeconomic news releases induce both information-related herding and herding without any reference to fundamentals. These results suggest that motives of investors herding behaviour vary under specific market conditions and share characteristics.
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.
This study applies a set of measures developed by Diebold and Yilmaz (2012, 2016) to examine connectedness via return and volatility spillovers across six large cryptocurrencies from August 7, 2015 to February 22, 2018. Regardless of the sign of returns, the results show that Litecoin and Bitcoin are at the centre of the connected network of returns. This finding implies that return shocks arising from these two cryptocurrencies have the most effect on other cryptocurrencies. Further analysis shows that connectedness via negative returns is largely stronger than via positive ones. Ripple and Ethereum are the top recipients of negative-return shocks, whereas Ethereum and Dash exhibit very weak connectedness via positive returns. Regarding volatility spillovers, Bitcoin is the most influential, followed by Litecoin; Dash exhibits a very weak connectedness, suggesting its utility for hedging and diversification opportunities in the cryptocurrency market. Taken together, results imply that the importance of each cryptocurrency in return and volatility connectedness is not necessarily related to its market size. Further analyses reveal that trading volume and global financial and uncertainty effects as well as the investment-substitution effect are determinants of net directional spillovers. Interestingly, higher gold prices and US uncertainty increase the net directional negative-return spillovers, whereas they do the opposite for net directional positive-return spillovers. Furthermore, gold prices exhibit a negative sign for net directional-volatility spillovers, whereas US uncertainty shows a positive sign. Economic actors interested in the cryptocurrency market can build on our findings when weighing their decisions.
The cryptocurrency market is unique on many levels: Very volatile, frequently changing market structure, emerging and vanishing of cryptocurrencies on a daily level. Following its development became a difficult task with the success of cryptocurrencies (CCs) other than Bitcoin. For fiat currency markets, the IMF offers the index SDR and, prior to the EUR, the ECU existed, which was an index representing the development of European currencies. Index providers decide on a fixed number of index constituents which will represent the market segment. It is a challenge to fix a number and develop rules for the constituents in view of the market changes. In the frequently changing CC market, this challenge is even more severe. A method relying on the AIC is proposed to quickly react to market changes and therefore enable us to create an index, referred to as CRIX, for the cryptocurrency market. CRIX is chosen by model selection such that it represents the market well to enable each interested party studying economic questions in this market and to invest into the market. The diversified nature of the CC market makes the inclusion of altcoins in the index product critical to improve tracking performance. We have shown that assigning optimal weights to altcoins helps to reduce the tracking errors of a CC portfolio, despite the fact that their market cap is much smaller relative to Bitcoin. The codes used here are available via www.quantlet.de.
This paper measures interdependencies among 18 major cryptocurrencies and shows that (i) Bitcoin is the dominant contributor of return and volatility spillovers among all the sampled cryptocurrencies; (ii) return and volatility spillovers have risen steadily over time; (iii) there are 'spikes’ in spillovers during major news events regarding cryptocurrencies. These findings suggest growing interdependence among cryptocurrencies and, by extension, a higher degree of contagion risk. It may be the case that cryptocurrencies are becoming more integrated, albeit this makes for interesting future empirical testing. In addition, the time-varying nature of spillovers reveals a certain dimension of uncertainty regarding the future of these digital currencies.
Is Bitcoin the new digital Gold? To answer this question, we investigate the potential benefits of Bitcoin during extremely volatile periods. To this end, we focus on the extreme correlation of asset returns estimated by multivariate extreme value theory. Considering first a position in equity markets, we find -similarly to previous studies- that the correlation of extreme returns increases during stock market crashes and decreases during stock market booms. Then, by combining each equity market with Bitcoin, we find that the correlation of extreme returns sharply decreases during both market booms and crashes, indicating that Bitcoin can play an important role in asset management. A similar result is obtained for Gold confirming its well-recognized status of a safe haven when a crisis happens. Furthermore, we find a low extreme correlation between Bitcoin and Gold, which implies that both assets can be used together in turbulent times in financial markets. Such evidence indicates that Bitcoin can be considered as the new digital Gold. However, Gold can still play an important role in portfolio risk management.
We analyse the existence of herding in the cryptocurrency market through the cross-sectional standard (absolute) deviation of returns. Our results show that extreme dispersion of returns is explained by rational asset pricing models although it is possible to observe herding during down markets, which highlights the inefficiency and risk of cryptocurrencies. We also observe that the smallest digital currencies are herding with the largest ones, thus traders base their decisions on the performance of the main cryptocurrencies. However, the herding phenomenon cannot be solely attributed to Bitcoin, since the rest of the market is not herding with the main cryptocurrency.
This paper analyzes the connectedness between forex and cryptocurrencies using the quantile cross-spectral approach. The sample covers six forex and six cryptocurrencies over the period of September 2015–December 2017. Compared with the results obtained from standard correlations and DMCA, the quantile cross-spectral approach provides richer information on the dependence structure across different quantiles and frequencies. The results show that there are some significant negative dependencies between forex and cryptocurrencies from both the short- and long-term perspectives; thus, it is worth diversifying between these two asset groups. Moreover, the connection between cryptocurrencies is not as strong as is widely believed.
The study reexamines the issue of informational efficiency of Bitcoin using data at different frequencies (15, 30, 60 and 120 min and daily data). In particular, we test the martingale hypothesis in Bitcoin returns using different variance ratio tests. We also examine the evolution of informational efficiency of Bitcoin using non-overlapping and overlapping moving window analysis. The study provides evidence of the presence of informational inefficiency in the Bitcoin market at higher frequency levels. The daily Bitcoin returns which appear to be following a memory-less stochastic process are in fact otherwise when we move to the higher frequencies of Bitcoin prices.
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.
This study explores the impacts of structural breaks (SB) on the dual long memory levels of Bitcoin and Ethereum price returns. We identify dual long memory and structural changes on cryptocurrency markets using four different generalized autoregressive conditional heteroskedasticity models (e.g., GARCH, FIGARCH, FIAPARCH, and HYGARCH). Furthermore, the persistence level of both returns and volatility equations decreases after accounting for long memory and switching states. Finally, the FIGARCH model with SB variables provides a comparatively superior forecasting accuracy performance. These findings have significant implications for both cryptocurrency allocations and portfolio management.
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.