Usama Adnan Fendi, Asem Tahtamouni, Yaser Jalghoum, Suleiman Jamal Mohammad
Bitcoin is an online communication system that facilitates the use of virtual currency, including electronic payments. This paper aims at analyzing the behavior of Bitcoin returns as a proposal for future currencies while making a comparison between Bitcoin and other conventional currencies. This paper uses quantitative approach to analyze the time series of Bitcoin and that of other conventional currencies during the period 2010–2018. It uses 1) a descriptive statistics for the weekly returns for Bitcoin which includes the mean, standard deviation, maximum value, minimum value, skewness, kurtosis, and Jarque-Bera normal distribution test statistics, and 2) duration dependence test on Bitcoin weekly returns by extracting the weekly returns for the Bitcoin that behave in irregular way of the general Bitcoin return level through autocorrelation regression, and taking the residuals for this regression as a time series for irregular returns.This paper has confirmed no empirical evidence for the existence of a speculative bubble in the Bitcoin values and returns. In addressing the question of whether Bitcoin can act as a reliable substitute for conventional currencies, the returns based analysis shows a huge difference between the behavior of Bitcoin returns from conventional currency returns when comparing both aspects of level and stability. The paper concluded that bitcoin is more an investment than a currency. This paper represents a significant contribution in the path of financial economics and financial risk management, and represents a contribution to the stability of the financial system around the world and mitigating financial crises.
This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. Tether (USDT) and TrueUSD (TUSD) are consistent outliers with respect to their returns, while Holo (HOT), NEXO (NEXO), Maker (MKR) and NEM (XEM) are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis.
This paper studies simple moving average trading strategies employing daily price data on the eleven most-traded cryptocurrencies in the 2016–2018 period. Our results indicate a variable moving average strategy is successful when using the 20 days moving average trading strategy. Specifically, excluding Bitcoin the technical trading rule generates an excess return of 8.76% p.a. after controlling for the average market return. Our results suggest that cryptocurrency markets are inefficient.
We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we solve explicitly for miners maximizing exponential utility and handle numerically in the case of miners with power utilities. We show that the heterogeneity of their initial wealth distribution leads to greater imbalance of the reward distribution, and increased wealth heterogeneity over time, or a “rich get richer” effect. This concentration phenomenon is aggravated by a higher Bitcoin mining reward and reduced by competition. Additionally, an advantaged miner with cost advantages such as access to cheaper electricity, contributes a significant amount of computational power in equilibrium, unaffected by competition from less efficient miners. Hence, cost efficiency can also result in the type of centralization seen among miners of cryptocurrencies. This paper was accepted by Kay Giesecke, finance. Funding: A. M. Reppen is partly supported by the Swiss National Science Foundation [Grant SNF 181815]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4798 .
Ikhlaas Gurrib, Qian Long Kweh, Mohammad Nourani, Irene Wei Kiong Ting
This study analyses whether returns of top market capitalised cryptocurrencies are affected by their movements or major global macroeconomic news. Daily data are collected for the leading 10 cryptocurrencies from July 2017–December 2018. This study, (i) tests whether lagged variables can help predict other variables’ returns through a vector autoregression (VAR) model, (ii) analyses the response of cryptocurrencies to one standard deviation shock on Bitcoin’s returns, and (iii) decomposes factors that contribute to variance and tests for structural breaks. Findings show that most cryptocurrencies do not significantly affect other variances, except for Monero, which represented between 19% and 45% of the variances of five cryptocurrencies. Autoregressive (AR) models are superior in forecasting one day ahead return forecasts, compared to the VAR model, whereas the random walk (RW) model ranked last. Although remarkable structural breaks are observed via impulse response functions during December 2017–January 2018, no major news announcements were released on the same day the breaks occurred. Overall, this study suggests the need for high-frequency cryptocurrency prices to tackle the issue of the relationship between intraday news release and cryptocurrencies.
We show that the level of market-efficiency in the five largest cryptocurrencies is highly time-varying. Specifically, before 2017, cryptocurrency-markets are mostly inefficient. This corroborates recent results on the matter. However, the cryptocurrency-markets become more efficient over time in the period 2017–2019. This contradicts other, more recent, results on the matter. One reason is that we apply a longer sample than previous studies. Another important reason is that we apply a robust measure of efficiency, being directly able to determine if the efficiency is significant or not. On average, Litecoin is the most efficient cryptocurrency, and Ripple being the least efficient cryptocurrency.
This paper examines the relationship between Inverse Perpetual Swap contracts, a Bitcoin derivative akin to futures and the margin funding interest rates levied on BitMEX. This paper proves the Heteroskedastic nature of funding rates and goes onto establish a causal relationship between the funding rates and the Bitcoin inverse Perpetual swap contracts based on Granger causality. The paper further dwells into developing a predictive model for funding rates using best-fitted GARCH models. Implications of the results are presented, and funding rates as a predictive tool for gauging the market trend is discussed.
Cryptomonnaies et efficience des marchés Les innovations apportées par les cryptomonnaies et leur technologie sous-jacente, la blockchain, ouvrent de nouvelles voies de recherches en finance. Cette thèse de doctorat est composée de trois essais portant sur les cryptomonnaies et est centrée autour de la notion d’efficience informationnelle des marchés. La première étude vise à expliquer comment la blockchain, développée au sein de communautés informelles, est adoptée et intégrée par les organisations. Cette étude apporte un cadre théorique à la technologie blockchain, cadre qui s’appuie sur les approches contractuelle et cognitive de la théorie des organisations. Grâce à une revue de la littérature illustrée, une analyse à deux dimensions présente les possibles utilisations de la blockchain fondées sur l’accès à l’information pour les participants. L’objectif de la seconde étude est double. Premièrement, elle soulève la problématique de la réelle nature du Bitcoin. Après avoir comparé le Bitcoin aux monnaies, à l’or et aux actions, nous basons notre analyse sur l’hypothèse que les cryptomonnaies peuvent être assimilées aux actions. Deuxièmement, la performance financière (la rentabilité ajustée au risque) du Bitcoin est mesurée en utilisant des modèles traditionnels tels que le MEDAF et le model de Fama-French à trois facteurs. Nous trouvons que l’intégration du Bitcoin dans un portefeuille améliore considérablement sa diversification, tout en apportant des rentabilités ajustées au risque positives et significatives dans le monde, l’Europe et l’Asie-Pacifique. La forte volatilité du Bitcoin ainsi que sa haute performance nous conduisent à analyser le caractère de bulle spéculative des cryptomonnaies, ce qui est l'objet de la troisième étude. Nous analysons cet aspect en utilisant le modèle PSY de Phillips and Shi, 2018. Deuxièmement, nous analysons le plus important pic/éclatement du marché des cryptomonnaies à la fin des années 2017 à l’aide du modèle LPPL (Log Periodic Power Law). Les résultats suggèrent des périodes de bulles avec effet de contagion entre les cryptomonnaies. Les analyses théoriques et empiriques de cette thèse contribuent à la littérature académique sur les cryptomonnaies. Nos résultats sont également importants pour les entreprises et pour les investisseurs qui s’intéressent au potentiel des cryptomonnaies et de la blockchain, ainsi que pour les décideurs politiques responsables de leur régulation.
Ferenc Béres, István András Seres, András A. Benczúr
Lightning Network (LN) is designed to amend the scalability and privacy issues of Bitcoin. It's a payment channel network where Bitcoin transactions are issued off chain, onion routed through a private payment path with the aim to settle transactions in a faster, cheaper, and private manner, as they're not recorded in a costly-to-maintain, slow, and public ledger. In this work, we design a traffic simulator to empirically study LN's transaction fees and privacy provisions. The simulator relies on publicly available data of the network structure and generates transactions under assumptions we attempt to validate based on information spread by certain blog posts of LN node owners. Our findings on the estimated revenue from transaction fees are in line with widespread opinion that participation is economically irrational for the majority of large routing nodes who currently hold the network together. Either traffic or transaction fees must increase by orders of magnitude to make payment routing economically viable. We give worst-case estimates for the potential fee increase by assuming strong price competition among the routers. We estimate how current channel structures and pricing policies respond to a potential increase in traffic, how reduction in locked funds on channels would affect the network, and show examples of nodes who are estimated to operate with economically feasible revenue. Even if transactions are onion routed, strong statistical evidence on payment source and destination can be inferred, as many transaction paths only consist of a single intermediary by the side effect of LN's small-world nature. Based on our simulation experiments, we quantitatively characterize the privacy shortcomings of current LN operation, and propose a method to inject additional hops in routing paths to demonstrate how privacy can be strengthened with very little additional transactional cost.
Stanisław Drożdż, Ludovico Minati, Paweł Oświȩcimka, Marek Stanuszek · 5 authors
Cross correlations in fluctuations of the daily exchange rates within the basket of the 100 highest-capitalization cryptocurrencies over the period October 1, 2015-March 31, 2019 are studied. The corresponding dynamics predominantly involve one leading eigenvalue of the correlation matrix, while the others largely coincide with those of Wishart random matrices. However, the magnitude of the principal eigenvalue, and thus the degree of collectivity, strongly depends on which cryptocurrency is used as a base. It is largest when the base is the most peripheral cryptocurrency; when more significant ones are taken into consideration, its magnitude systematically decreases, nevertheless preserving a sizable gap with respect to the random bulk, which in turn indicates that the organization of correlations becomes more heterogeneous. This finding provides a criterion for recognizing which currencies or cryptocurrencies play a dominant role in the global cryptomarket. The present study shows that over the period under consideration, the Bitcoin (BTC) predominates, hallmarking exchange rate dynamics at least as influential as the U.S. dollar (USD). Even more, the BTC started dominating around the year 2017, while other cryptocurrencies, such as the Ethereum and even Ripple, assumed similar trends. At the same time, the USD, an original value determinant for the cryptocurrency market, became increasingly disconnected, and its related characteristics eventually started approaching those of a fictitious currency. These results are strong indicators of incipient independence of the global cryptocurrency market, delineating a self-contained trade resembling the Forex.
Blockchain networks and similar cryptoeconomic networks are systems, specifically complex systems. They are adaptive networks with multiscale spatio-temporal dynamics. Individual actions may be incentivized towards a collective goal with “purpose-driven” tokens. Blockchain networks, for example, are equipped cryptoeconomic mechanisms that allow the decentralized network to simultaneously maintain a universal state layer, support peer-to-peer settlement, and incentivize collective action. These networks represent an institutional infrastructure upon which socioeconomic collaboration is facilitated – in the absence of intermediaries or traditional organizations. They provide a mission-critical and safety-critical regulatory infrastructure for autonomous agents in untrusted economic networks. Their tokens provide a rich, real-time data set reflecting all economic activities in their systems. Advances in network science and data science can thus be leveraged to design and analyze these economic systems in a manner consistent with the best practices of modern systems engineering. Research that reflects all aspects of these socioeconomic networks needs (i) a complex systems approach, (ii) interdisciplinary research, and (iii) a combination of economic and engineering methods, here referred to as “economic systems engineering,” for the regulation and control of these socioeconomic systems. This manuscript provides a conceptual framework synthesizing the research space and proceeds to outline specific research questions and methodologies for future research in this field, applying an inductive approach based on interdisciplinary literature review and relative contextualization of the works cited.
Sung Min Jang, Eojin Yi, Woo Chang Kim, Kwangwon Ahn
This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.
This paper provides a systematic survey on return and volatility spillovers of cryptocurrencies based on the empirical results of relevant academic literature. Evidence reveals that Bitcoin is the most influential among digital coins mainly as a transmitter toward digital currencies but also as a receiver of spillovers from virtual currencies and alternative assets. Ethereum, Litecoin, and Ripple present the most significant interlinkages with Bitcoin. Return spillovers are more pronounced but volatility spillovers often present a bi-directional character. Volatility shock transmission is detected among Bitcoin and national currencies, while economic policy uncertainty is not influential. This survey provides useful guidance in the hotly-debated issue of reform and decentralization of financial systems.
Van Minh Hao, Nguyen Huynh Huy, Bo Dao, Thanh-Tan Mai · 5 authors
Predicting cryptocurrency price movements is a challenging task due to the highly stochastic nature of the market. This paper exploits features from social media, combining with the historical price to build an accurate model for predicting trending of the Bitcoin, the most popular cryptocurrency these days. The novelty of this work is introducing a new feature called 'interaction' which helps improve the model's performance significantly. Our approach shows a very promising result, which outperforms other recent works by a large margin.
The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018 decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillover transmission during the analyzed time-frame, while Bittrexs act as a follower. We also found that connectedness of overall returns fell substantially right before the Bitcoin price hype, whereas it leveled out during the period the down market period. We confirmed that the results are robust with regards to the modeling strategies.
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.
This paper explores empirically the behavior of the Chicago Mercantile Exchange (CME) bitcoin futures contract. The analysis focuses on the time period between the launch of the CME bitcoin futures contract on December 18, 2017, and September 17, 2018. The behavior of the bitcoin spot market and CME futures market is compared and analyzed along several dimensions: price, volatility and liquidity. By comparing the Garman-Klass volatilities of bitcoin spot and futures prices with those of different assets, we find that both the bitcoin spot and futures markets exhibit relatively high volatility compared to other assets. When the ratio of trading volume over open interest is used to measure liquidity, the bitcoin futures market shows a mid-level liquidity. We also find while the exchange margin is set to meet the normal price volatility that can cover the daily price movements within one standard deviation, the brokerage margin for bitcoin futures is set at beyond two standard deviations. Some brokerage firms impose non-margin requirements such as high net account balance and open position limits in addition to regular margins. We conclude that the brokerage firms' relatively high margin and non-margin requirements impede trading activity such as short-sales and thus, liquidity and efficiency in the bitcoin futures market has been slow to develop.
We study the Bitcoin and Ether price series under a financial perspective. Specifically, we use two econometric models to perform a two-layer analysis to study the correlation and prediction of Bitcoin and Ether price series with traditional assets. In the first part of this study, we model the probability of positive returns via a Bayesian logistic model. Even though the fitting performance of the logistic model is poor, we find that traditional assets can explain some of the variability of the price returns. Along with the fact that standard models fail to capture the statistic and econometric attributes—such as extreme variability and heteroskedasticity—of cryptocurrencies, this motivates us to apply a novel Non-Homogeneous Hidden Markov model to these series. In particular, we model Bitcoin and Ether prices via the non-homogeneous Pólya-Gamma Hidden Markov (NHPG) model, since it has been shown that it outperforms its counterparts in conventional financial data. The transition probabilities of the underlying hidden process are modeled via a logistic link whereas the observed series follow a mixture of normal regressions conditionally on the hidden process. Our results show that the NHPG algorithm has good in-sample performance and captures the heteroskedasticity of both series. It identifies frequent changes between the two states of the underlying Markov process. In what constitutes the most important implication of our study, we show that there exist linear correlations between the covariates and the ETH and BTC series. However, only the ETH series are affected non-linearly by a subset of the accounted covariates. Finally, we conclude that the large number of significant predictors along with the weak degree of predictability performance of the algorithm back up earlier findings that cryptocurrencies are unlike any other financial assets and predicting the cryptocurrency price series is still a challenging task. These findings can be useful to investors, policy makers, traders for portfolio allocation, risk management and trading strategies.
We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist of several studies in finance. Popular labels given to market phases are “bull”, “bear”, “correction”, and “rally”. In the first part, we fit HMMs and HSMMs and look at the evolution of hidden state parameters and state persistence parameters over time to ensure that states are correctly classified in terms of market phase labels. We conclude that our modelling approaches yield positive results in both BTC/USD and the S and P 500, and both are best modelled via four-state HSMMs. However, the two assets show different regime volatility and persistence patterns—BTC/USD has volatile bull and bear states and generally weak state persistence, while the S and P 500 shows lower volatility on the bull states and stronger state persistence. In the second part, we put our models to the test of detecting different market phases by devising investment strategies that aim to be more profitable on unseen data in comparison to a buy-and-hold approach. In both cases, for select investment strategies, four-state HSMMs are also the most profitable and significantly outperform the buy-and-hold strategy.
With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.
Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool forcryptocurrencies market practitioners.
In this paper, we measure the asymmetric volatility spillover among six virtual financial asset (VFA) markets from January 1, 2014, to September 30, 2017, using the volatility spillover index based on a Markov regime-switching vector autoregressive (VAR) model and conduct a static and dynamic analysis under different regimes. The static results show that asymmetric effects of total, internal and net volatility spillover, on average, exist in all six VFA markets under different regimes. The dynamic results show that total, directional, and net spillover have significantly asymmetric effects. Thus, the government should monitor the specific VFA regimes and improve market regulation.
In this paper we explores as to whether cryptocurrency returns exhibit asymmetric reverting patterns and we test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns. For these reason, we uses non-linear autoregressive and Markov-switching GARCH (SETAR-MSGARCH) models. We finds strong evidence of regime changes in the mean and GARCH process. In addition, we conclude that bad news and good news of the same size have same impacts for investors.