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 .
Jan Jakub Szczygielski, Andreas Karathanasopoulos, Adam Zaremba
We perform the most comprehensive test of cryptocurrency return distributions to date. We fit 58 hypothetical distributions to 15 major cryptocurrencies to establish which of these best describes cryptocurrency returns. The answer is: ‘It depends.’ A sharp-peaked Cauchy distribution is the most likely distribution for the majority of return series. Specific distributions are definitively identified for only a handful of cryptocurrencies. The best fitting distributions are peaked and thick-tailed, with some possessing variable shape parameters. Our findings have implications for financial modelling and its applications, such as risk measurement and risk management.
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.
Cryptocurrency market is very volatile, trading prices for some tokens can experience a sudden spike up or downturn in a matter of minutes. As a result, traders are facing difficulty following with all the trading price movements unless they are monitoring them manually. Hence, we propose a real-time alert system for monitoring those trading prices, sending notifications to users if any target prices match or an anomaly occurs. We adopt a streaming platform as the backbone of our system. It can handle thousands of messages per second with low latency rate at an average of 19 seconds on our testing environment. Long-Short-Term-Memory (LSTM) model is used as an anomaly detector. We compare the impact of five different data normalisation approaches with LSTM model on Bitcoin price dataset. The result shows that decimal scaling produces only Mean Absolute Percentage Error (MAPE) of 8.4 per cent prediction error rate on daily price data, which is the best performance achieved compared to other observed methods. However, with one-minute price dataset, our model produces higher prediction error making it impractical to distinguish between normal and anomaly points of price movement.
Current research shows that stock market price, collected as a type of time-series data, could be forecasted by machine learning. The pricing data of cryptocurrency could also be used to conduct time-series prediction by leveraging different models, such as Long Short-Term Memory, Bayesian regression, GLM/Random Forest. This paper compares some of the machine learning methods used in predicting the price of cryptocurrencies by illustrating the nature of cryptocurrency, data availability, model used, results associated, and challenges.
Understanding how complex system components interact and adapt to environment changes is critical for analyzing their emergent behavior and the various positive and negative effects of that emergent behaviors might have. Several modeling languages and frameworks have been proposed for the modeling of complex adaptive systems but few have been applied in practice beyond simple models such as flocks of birds and predator prey. In this paper, we model the adaptive behavior of various entities in a Bitcoin market. We employ CASTLE, a dedicated framework for the modeling of adaptability in complex adaptive systems. Contrary to existing models where realistic details are not included, we introduce the influence of price speculation and news on trader behavior and experiment with different trader behaviors under varying market conditions. Our analysis of a market of 1,000 initial traders shows the feasibility of our approach but also highlights future research challenges.
Bitcoin is a peer-to-peer (P2P) electronic currency that allows online payments around the world without the management of a third party. Many studies have been conducted on the performance prediction of the stock market; in particular, the Autoregressive Integrated Moving Average model (ARIMA) is one of the linear regressive models widely used in the time series. Nevertheless, as artificial intelligence has become a heated research topic in modern days, A number of studies have also shown that the Back-propagation Neural Network (BPNN) is very effective in prediction. Hence, this paper compares the ARIMA model with the BPNN model in the prediction of Bitcoin price.
Stephen Wooley, Andrew N. Edmonds, Arunkumar Bagavathi, Siddharth Krishnan
Explosive growth in the value of cryptocurrencies like Bitcoin and Ethereum in recent years has attracted the attention of many speculators. Unlike traditional currencies, cryptocurrencies are not backed by any government agencies resulting in prices being strongly influenced by public opinion. Understanding the relationship between cryptocurrency prices and the public sentiment can lead to improved predictions of price movement. In this paper, we give an exploratory analysis of a network of 24 Reddit communities related to Bitcoin, Ethereum, or other cryptocurrencies to analyze Bitcoin and Ethereum price movements. We engineer a set of 112 time series features from submissions and comments made on the selected subreddits, run Granger causality tests on engineered time series against cryptocurrency price movements, and use these time series to forecast the cryptocurrency price movements using classification models. Results from these models support the Granger causality test results showing that with only lagged price values and lagged values from a single Reddit data derived feature, the direction of Bitcoin and Ethereum price movements can be predicted with 74.2% and 73.1% accuracy respectively.
Bitcoin alongside other cryptocurrencies became one of the largest trends recently, due to its redefinition of the concept of money, and its price fluctuation. Especially on the social media, people keep discussing Bitcoin topics, consulting, and advising about cryptocurrency trading. This paper explores the relationship between Twitter feed on Bitcoin and sentiment analysis of it, comparing and evaluating different data mining classifiers and deep learning methods that might help in better sentiment classification of Bitcoin tweets, the study uses different language modeling approaches, such as tweet embedding and N-Gram modeling. We also evaluate the quality of automated sentiment classification in comparison to manually assigned sentiment labeling. The results show that the manual approach gives significantly better results in some datasets, and superior performance of MLP, WiSARD and decision tree methods. On the other hand, R-Auto Tweets Sentiment (RATS) gives more stable performance overall datasets. using time-series, we found partial correlation between Bitcoin price fluctuation and sentiment class accuracy fluctuations using different machine learning algorithms.
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.
Pankaj K. Jain, Thomas H. McInish, Jonathan Miller
Abstract We examine commonality in returns and volume for Bitcoin–fiat currency pairs, each trading in a country with a single time zone. Bitcoin has substantial volume and obeys the theory related to commonality, liquidity, and price discovery. We find evidence that one common factor explains 68% of the variance in hourly volume. Though trading is higher on weekdays, there is substantial weekend trading, reflecting high retail participation. Volume is higher on exchanges during local working hours, as seen in forex markets, supporting the view that trading patterns depend on the location of trade rather than the location of the asset traded.
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.
Abstract This study analyses the price movements of a select sample of cryptocurrencies and examines whether they are cointegrated and predictable using machine learning algorithm and Johansen Test. The study used daily historical trading data of 76 cryptocurrencies sourced from different cryptocurrency exchanges. A sub‐sample of six cryptocurrencies were chosen for the cointegration and machine learning analysis based on their market share, attractiveness to the investors and availability of data for the full sample period. The data records starting from April 29, 2013 to February 7, 2019 were considered for the study. An error correction model was estimated to investigate both the long‐run and short‐run dynamics between the cryptocurrency prices. The evidence from the error correction model estimates shows that there is a long‐run association between the prices of crypto currencies. The machine learning algorithm involving neural networks (multilayer perception) was used to comprehend the data patterns in the cryptocurrency price series, and the results show that the model fits well in identifying and predicting the data patterns. The study also examines the possible value drivers of cryptocurrencies by estimating a linear regression with a set of covariates, which include the cryptocurrency demand and supply interaction variables and financial variables such as the NZX/S&P 50 index and exchange rates. The linear model estimates confirm that cryptocurrency market fundamentals have an important impact on cryptocurrency prices; however, they do not support the prediction that financial fundamentals are the major value drivers of cryptocurrencies.
This thesis addresses the prediction problems associated with noncausal processes in cryptocurrency markets. Chapter one provides background on Bitcoin and cryptocurrencies in general. It begins by introducing four major cryptocurrencies. Then recent developments in economic research on Bitcoin are discussed. \n \nChapter two introduces a noncausal autoregressive process with Cauchy errors in application to the exchange rates of the Bitcoin electronic currency against the US Dollar. The dynamics of the daily Bitcoin/USD exchange rate series display episodes of local trends, which are modelled and interpreted as speculative bubbles. The structure of the Bitcoin market is described to give context for the presence of multiple bubbles in the exchange rate. The bubbles may result from the speculative component in the on-line trading. The Bitcoin/USD exchange rates are modelled and predicted. The mixed causal-noncausal autoregressive model is shown to better fit the data than the traditional purely causal model. A forecasting exercise using the noncausal model is then presented. \n \nChapter three examines the performance of nonlinear forecasts of noncausal processes from closed-form functional predictive density estimators. To examine the performance, time series are simulated with different conditional means and non-Gaussian distributions. The processes considered have the mixed causal-noncausal MAR(1,1)dynamics and both finite and infinite variance. The forecasts are assessed based on the forecast error behaviour and the goodness of fit of the estimated predictive density. The persistence in the noncausal component directly relates to the magnitude of the bubble effects in the time series and is found to have a meaningful impact on how forecastable the process is. To better predict bubbles the joint density of the forecast at horizon two is shown to be an effective graphical method to detect the outset of a bubble.
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.