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The relationship between arbitrage in futures and spot markets and Bitcoin price movements: Evidence from the Bitcoin markets
Abstract
We examine how investors arbitrage the Bitcoin spot and futures markets. Using intraday data of the Chicago Board Options Exchange, we reconstruct the actual arbitrage condition that investors confront. We find that there are few arbitrage profit opportunities in “normal” markets, but large arbitrage profit opportunities arise during Bitcoin market “crashes.”
... In recent years, there has been a growing interest in the Bitcoin futures market among researchers, with studies focusing on identifying and exploiting arbitrage opportunities. Hattori and Ishida (2021) observe that Bitcoin market crashes provide a profitable opportunity for arbitrage, which dissipates over time after the launch of Bitcoin futures, thereby increasing market efficiency. Similarly, De Blasis and Webb (2022) identify cash-andcarry arbitrage opportunities during market dislocations and suggestive evidence of spillover effects between quarterly and perpetual futures. ...
Purpose
Our analysis is targeted at researchers in the fields of economics and finance, and we place emphasis on the incremental contributions of each paper, key research questions, study methodology, main conclusions and data and identification tactics. By focusing on these critical areas, our review seeks to provide valuable insights and guidance for future research in this rapidly evolving and complex field.
Design/methodology/approach
This paper conducts a structured literature review (SLR) of Bitcoin-related articles published in the leading finance, economics and accounting journals between 2018 and 2023. Following Massaro et al. (2016), SLR is a method for examining a corpus of scholarly work to generate new ideas, critical reflections and future research agendas. The goals of SLR are congruent with the three outcomes of critical management research identified by Alvesson and Deetz (2000): insight, critique and transformative redefinition.
Findings
The present state of research on Bitcoin lacks coherence and interconnectedness, leading to a limited understanding of the underlying mechanisms. However, certain areas of research have emerged as significant topics for further exploration. These include the decentralized payment system, equilibrium price, market microstructure, trading patterns and regulation of Bitcoin. In this context, this review serves as a valuable starting point for researchers who are unacquainted with the interdisciplinary field of bitcoin and blockchain research. It is essential to recognize the potential value of research in Bitcoin-related fields in advancing knowledge of the interaction between finance, economics, law and technology. Therefore, future research in this area should focus on adopting innovative and interdisciplinary methods to enhance our comprehension of these intricate and evolving technologies.
Originality/value
Our review encompasses the latest research on Bitcoin, including its market microstructure, trading behavior, price patterns and portfolio analysis. It explores Bitcoin's market microstructure, liquidity, derivative markets, price discovery and market efficiency. Studies have also focused on trading behavior, investors' characteristics, market sentiment and price volatility. Furthermore, empirical studies demonstrate the advantages of including Bitcoin in a portfolio. These findings enhance our understanding of Bitcoin's potential impact on the financial industry.
... It is advised that trading between exchanges, and specifically Bitcoin for other assets, may be more beneficial [13]. Some arbitrage opportunities might occur due to a market crash, but those are rare [14]. The better principle seems to be reliance on the decentralized nature of Blockchain technology, which makes the fundamentals of cryptocurrencies feasible and supports the emergence of exploitable arbitrages [15]. ...
Cryptocurrency arbitrage, a riskless trading strategy, can yield profits but requires swift execution due to volatile opportunities that vanish rapidly. Utilizing arbitrage bots for algorithmic trading is essential for immediate trade execution across exchanges like Binance and Bybit. This paper implements such a system focusing on BTCUSDT and ETHUSDT pairs. Integrating Machine Learning (ML) aims to predict arbitrage occurrences in advance for faster trade execution, a tactic many traders overlook. Logistic Regression, Random Forest, Support Vector Machine, and Multilayer Perceptron models are implemented. Adding ML principles required the collection of a dataset with historical prices of the observed cryptocurrency pairs for various time intervals, on which we trained the model. Afterward, the model was evaluated in a live-trading environment. Results show Random Forest predicting exploitable arbitrage intervals ahead for Ether, with ML models more effective during less volatile periods. However, careful consideration is needed as predictions may not always align with market realities, leading to mixed trading outcomes. Furthermore, the training led to a model that can predict the occurrence of arbitrage; however, classifying the calculations even more carefully than in reality, resulting in a partially profitable or partially lossy trading strategy depending on the time of day and the current market stage.
... Past studies focused on different important and vital features of cryptocurrencies, such as speculative bubble behavior in Bitcoin's returns (Chaim and Laurini, 2019;Fendi et al., 2019), momentum effects after one-day abnormal return (Caporale and Plastun, 2020), opportunities and challenges of cryptocurrencies (Fauzi and Paiman, 2020), and speculative behavior of Bitcoin (Cheung et al., 2015). Additionally a most recent paper by Hattori and Ishida (2021) examines the arbitrage behaviors of the investors in the Bitcoin spot and futures markets and they report evidence in support of market efficiency. Baur et al. (2019) is the first study that examines the anomalies in prices and trading volumes of Bitcoin across seven different global cryptocurrency exchanges. ...
... Additionally, evidence reveals that there are large arbitrage opportunities during Bitcoin market crashes, between the Bitcoin spot and futures market (Hattori & Ishida, 2020). Further evidence reveals that Bitcoin presents information inefficiency, for 115-and 60-min returns. ...
This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology-the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies.
... The gray ones study other forms of arbitrage within the Bitcoin ecosystem. Fig. 8. Related work on arbitrage in temporal and market context (links for bibliography: Badev and Chen, 2014;Bistarelli et al., 2019;Hattori and Ishida, 2021;Hautsch et al., 2018;Kroeger and Sarkar, 2017;Krückeberg and Scholz, 2020;Lee et al., 2020;Pieters and Vivanco, 2017;Shynkevich, 2020). platforms (e.g., Borri and Shakhnov, 2022). ...
... Empirical studies obtain similar conclusions on the presence of arbitrage opportunities and agree that price deviations, even in different time epochs, emerge and are persistent (Badev and M. Chen, 2014;Pieters and Vivanco, 2017;Kroeger and Sarkar, 2017;Krückeberg and Scholz, 2020); evidence of mispricings across markets is found also in works proposing theoretical models on arbitrage which are fitted on empirical data (Hautsch, Scheuch, and Voigt, 2018;Bistarelli et al., 2019). Other recent studies focus instead on the nascent futures market for Bitcoin: Hattori and Ishida (2021), Shynkevich (2020), and S. Lee, El Meslmani, and Switzer (2020) obtain partially contrasting findings on the efficiency of such markets (specifically, the first two sources find evidence of efficiency in the markets; the disagreement with the tenor of most other literature can be attributed to the time window and the fact that the futures market operates in a single geographical area). For completeness, we mention that other studies investigate more broadly arbitrage in the cryptocurrency market (e.g., Gandal and Halaburda, 2014;Fischer, Krauss, and Deinert, 2019;Leung and Nguyen, 2019;Crépellière and Zeisberger, 2020). ...
We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage activity conducted within the platform. The availability of user identifiers per trade allows us to focus on the historical record of 440 investors, detected as arbitrageurs, and consequently to describe their trading behavior. We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account. In particular, we distinguish between those who conducted arbitrage in a single or in multiple markets: using this element as a proxy for trade ability, we find that arbitrage actions performed by expert users are on average non-profitable when transaction costs are accounted for, while skilled investors conduct arbitrage at a positive and statistically significant premium. Next, we show that specific trading strategies, such as splitting orders or conducting arbitrage non aggressively, are further indicators of expertise that increase the profitability of arbitrage. Most importantly, we exploit within-user (across hours and markets) variation and document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. We present further evidence that such differences are chiefly due to a better ability of the latter in incorporating information, both on the transactions costs and on the exchange rates volatility, eventually resulting in a better timing choice at small time scale intervals. Our results support the hypothesis that arbitrageurs are few and sophisticated users.
... This may be the result of more arbitrage activities in the bitcoin market during the crisis. As demonstrated by Hattori and Ishida (2020), there are more arbitrage opportunities in the bitcoin market during market crashes. ...
This paper adopts the fractional cointegrated vector autoregressive (FCVAR) model to examine high‐frequency price discovery of bitcoin spot and futures prices from December 18, 2017 to July 31, 2020. We find that bitcoin spot and futures prices exhibit long memory properties and they are fractionally cointegrated. The result shows that the bitcoin futures market dominates the price discovery process. Interestingly, during the Covid‐19 pandemic, the bitcoin price discovery leadership has switched to the spot market. Moreover, we find that the bitcoin futures market follows a long‐run contango. The nonfractional CVAR model overestimates the price discovery of the futures market.
... Therefore, the absence of such mechanisms creates the opportunities for arbitrageurs to trade across different markets. Comparatively, Ref. [18] investigated arbitrage opportunities between bitcoin cash and future markets. The authors determined that although arbitrage opportunities prevailed between December 2017 and February 2018, such opportunities faded away thereafter. ...
The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.
... We categorise global Bitcoin trading into five segmented markets by the base currency against which Bitcoin is traded, namely, Australia dollar (AUD), Canadian dollar (CAD), Euro (EUR), British pound (GBP), and US dollar (USD). 2 Using daily data from 1st 1 See, e.g. Bariviera (2017), Hattori and Ishida (2020), Kroeger and Sarkar (2017), Nadarajah and Chu (2017), Urquhart (2016), Wei (2018), Zargar and Kumar (2019). 2 Our categorisation ensures a sample that is liquid and spans a long enough period that allows for a meaningful dynamic efficiency analysis by rolling-window estimations. This categorisation is also justified by the fact that most Bitcoin traders use only one fiat currency, usually their home currency, January 2013 to 7th January 2020 for the five Bitcoin markets, our study is conducted in three steps. ...
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.
... This may be the result of more arbitrage activities in the bitcoin market during the crisis. As demonstrated by Hattori and Ishida (2020), there are more arbitrage opportunities in the bitcoin market during market crashes. ...
We apply the fractionally cointegrated vector autoregressive (FCVAR) model to analyze the relationship between spot and futures prices in five commodity markets (aluminium, copper, lead, nickel, and zinc). To this end, we first extend the FCVAR model to accommodate deterministic trends in the levels of the processes. The methodological contribution is to provide representation theory for the FCVAR model with deterministic trends, where we show that the presence of the deterministic trend in the process induces both restricted and unrestricted constant terms in the vector error correction model. The consequences for the cointegration rank test are also briefly discussed. In our empirical application we use the data from [9], who conduct a similar analysis using the usual (non-fractional) cointegrated VAR model. The main conclusion from the empirical analysis is that, when using the FCVAR model, there is more support for the cointegration vector (1, − 1) ′ in the long-run equilibrium relationship between spot and futures prices, and hence less evidence of long-run backwardation, compared to the results from the non-fractional model. Specifically, we reject the hypothesis that the cointegration vector is (1, − 1) using standard likelihood ratio tests only for the lead and nickel markets.
This paper provides a first economic analysis of liquid staking tokens, which are derivatives representing a share of staked tokens in Proof‐of‐Stake blockchains. We document substantial time‐variation in the “liquid staking basis” as given by the price difference between a derivative staking token and its underlying cryptocurrency. We find evidence that staking rewards, concentration risks, limits to arbitrage, and behavioral factors influence this basis. The liquid staking basis is wider when the yields offered by the liquid staking protocol are low relative to the alternative of staking directly, when cryptocurrency returns are more volatile, and when secondary market liquidity is low. In contrast, it is smaller when investors pay more attention to liquid staking and when investor sentiment is positive. Furthermore, liquid staking tokens contribute a significant and overall growing amount to price discovery in the underlying cryptocurrencies.
The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.
In this study will discuss about the important issues related to balance of trade of Pakistan by covering the time period 2000 to 2023. It is analyzed that there is positive impact of market size and infrastructure on balance of trade of Pakistan where is exchange rate is impacting negatively for balance of trade of Pakistan. The data is analyzed through correction mechanism and co-integration after testing the stationery of data. Few important policy directions are given on the basis of available data that told about core variables of economy that may impact in right direction for economic analysis of Pakistan. In addition to this the annual data is utilized for policy making in this studies.
This study will work on some core variables including exchange rate that is considered as an important variable for different nations and size of level of development. In economics different functions are impacted here by exchange rate changes and it is very important to analyze that the macroeconomic factors including interest rate, inflation rate may impact on exchange rate positively or negatively. For the economy of Pakistan covering the period 2000 to 2023 it is analyzed that the multiple regression model by using exchange rate, inflation rate, interest rate, the value of exports and the value of imports in Pakistan having some important results for country. This study has used unit root test co-integration test and ARDL for evaluation of short run and long run effect of some core variables. This research found and concluded that all the right side variables in multiple regression model having some important impact on relationship for the determination of exchange rate and it is explored that the inflation rate interest rate import and export are captured to regressive exchange rate in the economy of Pakistan.
This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.
This paper presents an empirical analysis of bitcoin futures risk premia. Based on the relevant theories and empirical findings of commodity futures risk premia, we study a battery of predictors, including position‐based measures, market microstructure factors, and macroeconomic variables. We find that trading activity and extreme sentiment of speculators and retailers present significant predicting power on the subsequent bitcoin futures price changes over different time horizons. We also find evidence that the lower transaction cost, the higher bitcoin futures risk premiums. Regarding macroeconomic variables, financial conditions index, TED spread, US M2 money stock, and funding cost of financial institutions could predict bitcoin futures returns. The return impact of net position changes of hedgers is likely to be affected by extremes in macroeconomic variables. Speculators behave like negative feedback traders, while retailers are positive feedback traders. This detailed analysis of the risk premia of this emerging derivatives market provides critical implications.
In order to avoid the risks brought by blind investment, it is necessary to forecast the trading price of gold and bitcoin on that trading day before investment. To aim at this prediction problem, this paper puts forward the price prediction model of the BP neural network. The experimental results show that the model can reasonably predict the trading price of gold and bitcoin on the next trading day, and the relative error between them is less than 10%.However, predicting the trading price of gold and bitcoin can roughly determine the future direction, and it cannot provide scientific and reasonable trading strategies for traders, so it is necessary to design a trading strategy planning model. Considering that every trading activity except holding financial products will charge additional trade costs, the trade costs of gold and bitcoin are 1% and 2% of the trade amount, respectively. By comparing various classic trading models, we propose to use a dynamic programming model to provide traders with effective trading strategies, which takes the trading changes of gold and bitcoin in the next three trading days as decision variables. Meanwhile, it takes the highest value of the Sharp ratio in the next trading day as the objective function. It takes the proportion of each part as equal to or greater than 0 as the constraint condition to establish a trading strategy planning model to obtain the maximum profit.
Perpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple “u‐shaped” curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations.
In this article, we investigate the quantitative risk management of Bitcoin futures by using the GARCH models. We first found that it is crucial to introduce a heavy-tailed distribution into the GARCH models to explain return volatilities of Bitcoin futures. Then, we compare the VaR estimates based on the parametric methods, namely the GARCH model with the normal distribution (GARCH-Normal) and the GARCH model with the normal inverse Gaussian distribution (GARCH-NIG), and the nonparametric method. Our results illustrate that although the VaR estimates based on the nonparametric method are overall accurate and even more accurate than the VaR estimates based on the GARCH-Normal model, the VaR estimates based on the GARCH-NIG model perform the best. Overall, we conclude that the GARCH-NIG model could generate accurate VaR estimates for the Bitcoin futures return series. In addition, we found that in contrast to Bitcoin cash, the return volatilities of the Bitcoin futures do not increase by more in response to positive shocks than in response to negative shocks.
In this study, we first investigate the volatility term structure of the Bitcoin futures prices and observe that the price volatilities of Bitcoin futures contracts decrease as the delivery date nears, which is opposite to the Samuelson effect frequently observed in the commodity market. Then, we modify the stochastic multifactor model in Schwartz and Smith (2000) and fit it to the Bitcoin futures prices data. The results show that the standard stochastic multifactor model performs well in explaining the fluctuations of Bitcoin futures prices and generates an upward volatility term structure of the Bitcoin futures contracts prices. Nevertheless, some of the parameter's estimations exhibit non-negligible differences compared with those based on other commodity futures prices data in the existing literature, which indicates that the Bitcoin futures market might be different from the standard commodity futures market.
At the end of 2017, the Bitcoin price dropped significantly by approximately 70% over the two months. Since the introduction of Bitcoin futures coincided with this market crash, it is said that the new financial instrument might have caused the market crash. The literature states that the futures enabled investors to easily take a short position and hypothesizes that the selling pressure from futures could have potentially crashed the Bitcoin market. To evaluate this assumption, we investigate the empirical relationship between futures trading and the Bitcoin price by using high-frequency data. We find that Bitcoin futures trading was not significantly related to the returns on Bitcoin futures and spot returns. Therefore, we conclude that Bitcoin futures did not lead to the crash of the Bitcoin market at the end of 2017.
We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find long-memory in both the individual markets and the system of markets depicting non-homogeneous informational inefficiency. Moreover, Bitcoin markets are found to be fractionally cointegrated, where uncertainty negatively impacts this type of cointegration relationship.
Ethereum is an important blockchain, being the first and most popular public platform for the smart contracts underpinning financial transactions, time-stamping of supply chains, decentralized applications and initial coin offerings. Ethereum's cryptocurrency, ether, is actively traded on centralized exchanges, second only to bitcoin. It has attracted investor's interest primarily because of its intrinsic value – small units of ether called ‘gas’ are used, essentially, as the fuel driving smart contract transactions on the Ethereum blockchain. We ask whether off-chain trading on ether derivatives plays a dominant role in ether spot price discovery, thereby driving ether's utility value for on-chain activity. Using minute-by-minute data we find that the ether perpetual swap on BitMEX, an unregulated cryptocurrency derivative exchange, has dominant trading volume and price discovery over the major spot exchanges. Furthermore, we identify interesting hour-of-day and day-of-week effects in trading volume on the spot exchanges, and these indicate that more informed institutional players are trading ether spot and derivatives.
BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute‐by‐minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid–ask spreads, interexchange spreads, and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritize the investigation of the legitimacy of BitMEX and its contracts.
Cryptocurrency markets exhibit periods of large, recurrent arbitrage opportunities across exchanges. These price deviations are much larger across than within countries, and smaller between cryptocurrencies, highlighting the importance of capital controls for the movement of arbitrage capital. Price deviations across countries co-move and open up in times of large bitcoin appreciation. Countries with higher bitcoin premia over the US bitcoin price see widening arbitrage deviations when bitcoin appreciates. Finally, we decompose signed volume on each exchange into a common and an idiosyncratic component. The common component explains 80% of bitcoin returns. The idiosyncratic components help explain arbitrage spreads between exchanges.
We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find long-memory in both the individual markets and the system of markets depicting non-homogeneous informational inefficiency. Moreover, Bitcoin markets are found to be fractionally cointegrated, where uncertainty negatively impacts this type of cointegration relationship.
Considering a relatively large cross-section of ten cryptocurrencies, we test for the existence of well-known equity seasonality patterns with respect to cryptocurrency returns, volatility, trading volume and a spread estimator. Whilst we do not observe consistent and robust calendar effects in cryptocurrency returns and consequently cannot reject the weak-form market efficiency, we do observe robust patterns in trading activity. As such, trading volume, volatility and spreads are on average lower in January, on weekends and during the summer months. Besides, we also report a strong impact on the direction and significance of monthly seasonality patterns due to the stark market sell-off in January 2018, which has to be accounted for.
We examine the investibility of Bitcoin by exploring the trading dynamics and market microstructure of Bitcoin on three US cryptocurrency exchanges using high frequency intraday data of individual trades and quotes. Although all exchanges offer continuous trading, we find that the highest trading activity, highest volatility and lowest spreads coincide with US market trading hours, suggesting that most trades are non-algorithmic and executed by retail investors. We further find that average quoted and effective spreads for Bitcoin are lower than spreads on major equity exchanges, implying that Bitcoin is highly investible for retail size transactions.
Academic research on cryptocurrencies is almost exclusively directed towards Bitcoin. We extend existing literature by performing various tests on efficiency of several cryptocurrencies and additionally link efficiency to measures of liquidity. Cryptocurrencies become less predictable / inefficient as liquidity increases.
We revisit the issue of informational efficiency of Bitcoin using a battery of computationally efficient long-range dependence estimators for a period spanning over July 18, 2010 to June 16, 2017. We report that the market is informational efficient as consistent to recent findings of Urquhart (2016), Nadarajah and Chu (2017) and Bariviera (2017).
This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility.
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Urquhart (2016) investigated the market efficiency of Bitcoin by means of five different tests on Bitcoin returns. It was concluded that the Bitcoin returns do not satisfy the efficient market hypothesis. We show here that a simple power transformation of the Bitcoin returns do satisfy the hypothesis through the use of eight different tests. The transformation used does not lead to any loss of information.
Bitcoin has received much attention in the media and by investors in recent years, although there remains scepticism and a lack of understanding of this cryptocurrency. We add to the literature on Bitcoin by studying the market efficiency of Bitcoin. Through a battery of robust tests, evidence reveals that returns are significantly inefficient over our full sample, but when we split our sample into two subsample periods, we find that some tests indicate that Bitcoin is efficient in the latter period. Therefore we conclude that Bitcoin in an inefficient market but may be in the process of moving towards an efficient market.
This paper consolidates the results of some recent work on the relation between forward prices and futures prices. It develops a number of propositions characterizing the two prices. These propositions contain several testable implications about the difference between forward and futures prices. Many of the propositions show that equilibrium forward and futures prices are equal to the values of particular assets, even though they are not in themselves asset prices. The paper then illustrates these results in the context of two valuation models and discusses the effects of taxes and other institutional factors.
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
Empirical studies of the Treasury Bill markets have revealed substantial differences between the futures price and the implied forward price. These differences have been attributed to taxes, transaction costs, and the settling up procedure employed in the futures market. This paper examines the forward and futures prices in foreign exchange in an attempt to distinguish between the competing explanations.
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