Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

Changes in the market structure and risk management of Bitcoin and its forked coins

Inconsistency of consensus results in blockchain forks, which create a new financial risk. After filtering out Bitcoin’s linear, nonlinear, and lag impacts on forked coins, this study employs a bottom-up hierarchical clustering algorithm to examine the logarithmic return series for Bitcoin and its 14 forked coins from 2018 to 2021. The results indicate that the market for forked coins can be divided into three clusters: SegWit-supported forked coins, mature forked coins, and the latest forked coins. Bitcoin and the mature forked coins form a cluster, and its performance is superior to others. Although Bitcoin’s return significantly affects that of its forked coins, it does not affect the market structure. Furthermore, this study provides references for risk aversion among investors in forked coins and presents macro-level information for cryptocurrency market authorities.     

A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets

This study examines time–frequency relationship between Bitcoin prices and Bitcoin mining based on daily data from January 2013 to October 2018. Bitcoin mining is measured through Bitcoin hashrate, which represents the completion speed of the Bitcoin code. We also include three energy commodities, i.e. oil, coal, and gas in a multivariate model employing time–frequency wavelet extensions in the form of partial and multivariate models. Results of our study suggest that both oil and gas lead Bitcoin returns from mid 2014 till 2016 across 64– 128 days' period. Under the investment period of 64– 256, hashrate and Bitcoin returns share significant comovement in the presence of oil and natural gas however exhibit no comovement when the effect of coal market is considered. Our results of wavelet decomposition suggest that the magnitude of comovement ranging from short- to long-run is time varying. Finally, results of the causality on quantile test suggest that Bitcoin returns cause changes in Bitcoin hashrate mostly during median quantiles with an asymmetric pattern. Our work entail implications for investors in the Bitcoin and energy market and is also helpful in forecasting the pricing behavior of Bitcoin using the hashrate and vice versa.     

The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets

Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling.     

Capture the contagion network of bitcoin – Evidence from pre and mid COVID-19

COVID-19 is the first global scale crisis since the inception of Bitcoin. We compare the contagion phenomenon of Bitcoin and other financial markets or assets pre and during the COVID-19 shock in both contemporaneous and non-contemporaneous manner. This paper uses the directed acyclic graph (DAG), spillover index, and network topology to provide strong evidence on the directional contagion outcomes of Bitcoin and other assets. The empirical results show that the contagion effect between Bitcoin and developed markets is strengthened during the COVID-19 crisis. Particularly, European market has a dominant role. Excluding Bitcoin’s own shocks, United State and European markets are the main contagion sources to Bitcoin. European market also works as a intermediary to deliver infectious from United State and market fear. The findings show that gold always has contagion effect with Bitcoin, while gold, US dollar and bond market are the contagion receivers of Bitcoin under the shock of COVID-19. The empirical results further proved the safe haven, hedge and diversifier potential of Bitcoin in economic stable time, but also shows that the sustainability of these properties is undermined during the market turmoil.     

Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying

In this study, we analyze the properties of Bitcoin as a diversifier asset and hedge asset against the movement of international market stock indices: S&P500 (US), STOXX50 (EU), NIKKEI (Japan), CSI300 (Shanghai), and HSI (Hong Kong). For this, we use several copula models: Gaussian, Student-t, Clayton, Gumbel, and Frank. The analysis period runs from August 18, 2011 to June 31, 2019. We found that the Gaussian and Student-t copulas are best at fitting the structure dependence between markets. Also, these copulas suggest that under normal market conditions, Bitcoin might act as a hedge asset against the stock price movements of all international markets analyzed. However, the dependence on the Shanghai and Hong Kong markets was somewhat higher. Also, under extreme market conditions, the role of Bitcoin might change from hedge to diversifier. In a time-varying copula analysis, given by the Student-t copula, we found that even under normal market conditions, for some markets, the role of Bitcoin as a hedge asset might fail on a high number of days.     

Asymmetric mean reversion of Bitcoin price returns

Non-linearity is characterised by an asymmetric mean-reverting property, which has been found to be inherent in the short-term return dynamics of stocks. In this paper, we explore as to whether cryptocurrency returns, as represented by Bitcoin, exhibit similar asymmetric reverting patterns for minutely, hourly, daily and weekly returns between June 2010 and February 2018. We identify several differences in the behaviour of Bitcoin price returns in the pre- and post-$1000 sub-periods and evidence of asymmetric reverting patterns in the Bitcoin price returns under all the ANAR models employed, regardless of the data frequency considered. We also present evidence indicating stronger reverting behaviour of negative price returns in terms of both reverting speed and magnitude compared to positive returns and evidence of positive serial correlation with prior positive price returns. Finally, we also investigated asymmetries in Bitcoin price return series' persistence by employing higher order ANAR models, finding evidence of a higher persistence of positive returns than negative returns, a result that further supports the existence of asymmetric reverting behaviour in the Bitcoin price returns.     

Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach

Employing a long-memory approach, we provide a study of the evolution of informational efficiency in five major Bitcoin markets and its influence on cross-market arbitrage. While all the markets are close to full informational efficiency over the whole sample period, the degree of market efficiency varies across markets and over time. The cross-market discrepancy in market efficiency gradually vanishes, suggesting the segmented markets are developing to a consensus where all markets are equally efficient. Through a fractionally cointegrated vector autoregressive (FCVAR) model we show that when the efficiency in Bitcoin/USD and Bitcoin/AUD markets improves the cross-market arbitrage potential narrows, whereas it widens when the efficiency in Bitcoin/CAD, Bitcoin/EUR, and Bitcoin/GBP markets improves. A battery of robustness checks reassure our main findings.     

Tail dependence between Bitcoin and financial assets: Evidence from a quantile cross-spectral approach

Using daily price data for Bitcoin and 10 representative financial assets from the stock, commodity, gold, foreign exchange and bond markets from 2011 to 2019, we study the tail dependence between returns for Bitcoin and these other financial assets using the novel “quantile cross-spectral dependence” approach of Baruník and Kley (2019). We find evidence of right-tail dependence between Bitcoin returns and the S&P 500 in the long term and weaker normal return dependence between Bitcoin and the US Dollar (USD)–Euro (EUR) foreign exchange rate in the monthly term. In addition, we note that the dependence between Bitcoin and commodity as well as oil, and silver decrease the most within their respective medium return quantiles over the short term. Furthermore, we document a one-way causality running from each of the financial assets considered to Bitcoin in different quantiles of the return distribution. In sum, our findings support the notion that Bitcoin can provide financial diversification in certain return quantiles (i.e., bear, normal, or bull asset conditions) and time frequencies (i.e., short, medium, or long term investment horizon).     

Social media bots and stock markets

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.     

Critical dynamics related to a recent Bitcoin crash

The cryptocurrency market is an emerging market that is characterized by intense volatility and therefore the study of its dynamics presents increased interest. The present work investigates the issue of the critical dynamics of a financial complex system approaching a crash, by using the Method of Critical Fluctuation (MCF) which is known for its ability to uncover critical dynamics. Specifically, we study the recent crash that took place in the cryptocurrency market (starting on 12 May 2021), by analyzing the “Contracts for Difference” (CFDs) prices on Bitcoin/USD (Bitcoin to US-Dollar exchange rate) at five different high frequency trading time intervals (60, 30, 15, 5 and 1 min). The results show that, for the 60-min and 30-min sampling intervals, a specific sequence of indications is identified, in agreement to the evolution towards extreme events in other complex systems, such as earthquakes. This sequence of indications isn't maintained as the sampling frequency is increased. Notably, the existence of critical dynamics during the system's evolution has been detected both in equilibrium and out-of-equilibrium by means of the same analysis method (MCF). The obtained results indicate that the MCF could provide useful information for portfolio analysis and risk management.     

Bitcoin as a Complement to Emerging Market Currencies

Bitcoin is a digital currency that has gained significant traction as an economic instrument. Despite its rise, it has received little attention from the scholarly community. This study is one of the first studies to examine Bitcoin’s use as a complement to emerging markets currencies; more specifically, I analyze the value and volatility of Bitcoin relative to emerging market currencies and explore ways in which Bitcoin can complement emerging market currencies. The results suggest that Bitcoin has characteristics that make it well-suited to work as a complement to emerging market currencies and that there are ways to minimize Bitcoin’s risks.     

What role do futures markets play in Bitcoin pricing? Causality,cointegration and price discovery from a time-varying perspective?

Recent papers that have explored spot and futures markets for Bitcoin have concluded that price discovery takes place either in the spot, or the futures market. Here, we consider the robustness of previous price discovery conclusions by investigating causal relationships, cointegration and price discovery between spot and futures markets for Bitcoin, using appropriate daily data and time-varying mechanisms. We apply the time-varying Granger causality test of Shi, Phillips, and Hurn [2018]; time-varying cointegration tests of Park and Hahn [1999], and time-varying information share methodologies, concluding that futures prices Granger cause spot prices and that futures prices dominate the price discovery process.     

On the intraday return curves of Bitcoin: Predictability and trading opportunities

Motivated by the potential inferences from intraday price data in the controversial Bitcoin market, we apply functional data analysis techniques to study cumulative intraday return (CIDR) curves. First, we indicate that Bitcoin CIDR curves are stationary, non-normal, uncorrelated, but exhibit conditional heteroscedastic, although we find that the projection scores of CIDR curves could be serially correlated during some certain periods. Second, we show the possibility of predicting the CIDR curves of Bitcoins based on the projection scores and then assess the forecasting performance. Finally, we utilize the functional forecasting methods to explore the intraday trading opportunities of Bitcoins and the results provide evidence of profitable trading opportunities based on intraday trading strategies, which confronts the efficient market hypothesis.     

Social media and price discovery: The case of cross-listed firms

In this article, we examine whether social media information affects the price-discovery process for cross-listed companies. Using over 29 million overnight tweets mentioning cross-listed companies, we examine the role of social media for a link between the last periods of trading in the US markets and the first periods in the UK market. Our estimates suggest that the size and content of information flows on social networks support the price-discovery process. The interactions between lagged US stock features and overnight tweets significantly affect stock returns and volatility of cross-listed stocks when the UK market opens. These effects weaken and disappear 1 to 3 hr after the opening of the UK market. We also develop a profitable trading strategy based on overnight social media, and the profits remain economically significant after considering transaction costs.     

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

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

Can both Bitcoin and gold serve as safe-haven assets? — A comparative analysis based on the NARDL model

This paper uses the nonlinear autoregressive distributed lag (NARDL) model to analyze the different performances of Bitcoin and gold under the impacts of three different uncertainties, namely global economic policy uncertainty (GEPU), US stock market volatility index (VIX) and the CBOE crude oil ETF volatility index (OVX). The results indicate that faced with shocks of different uncertainties, Bitcoin is unable to serve as a safe-haven, while gold can hedge against uncertainties to varying degrees. Moreover, the three types of uncertainties have asymmetric impacts on the prices of Bitcoin and gold respectively. The decrease of uncertainties has a greater impact on Bitcoin price than the increase, while the increase of uncertainties has a greater impact on gold price than the decrease. It suggests that investors are cautious and optimistic about Bitcoin, and gold remains unanimously recognized as the traditional safe-haven.     

Does Bitcoin still own the dominant power? An intraday analysis

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

A systematic review of the bubble dynamics of cryptocurrency prices

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

A novel two-stage approach for cryptocurrency analysis

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