Ángeles López Cabarcos, Ada M. Pérez-Pico, Juan Piñeiro Chousa, Aleksandar Šević
Bitcoin is the cryptocurrency with the largest market capitalization, and many studies have examined its role in financial markets. In this manuscript, we contribute to the extant body of knowledge by analyzing the Bitcoin behavior and the effect that investor sentiment, S&P 500 returns, and VIX returns have on Bitcoin volatility using GARCH and EGARCH models. The results suggest that Bitcoin volatility is more unstable in speculative periods. In stable periods, S&P 500 returns, VIX returns, and sentiment influence Bitcoin volatility.
This paper aims to examine the relationship between Bitcoin and preeminent financial indicators using Copula-GARCH method. In the study, we use closing prices of Bitcoin and US 10-Year Bond Yield, Gold Spot US Dollar, US Dollar Index, S&P 500, FTSE 100 and NIKKEI 225. To our knowledge, our paper is the first to examine this issue empirically. Analysis results show that there is no strong interdependence between Bitcoin and preeminent financial indicators. These findings provide new information that will benefit policy makers, banks, financial investors, and risk managers in trading activities for both long-term and short-term strategies.
The study investigates the bitcoin-altcoin price synchronization hypothesis using cointegrating test and VEC Granger Causality/Block Exogeneity Test approaches on the daily data of bitcoin and ten selected alternative coins (altcoins) between August 8, 2015 and December 31, 2018. The data is structurally divided into three distinct periods which are; August 8, 2015 to December 31, 2016; January 1, 2017 to December 31, 2017 and January 1, 2018 to December 31, 2018. The study establishes pure price separation between the altcoin and bitcoin in 2015-2016, price synchronization between bitcoin and each of the selected altcoin in 2017 and dominant altcoin-to-bitcoin price formation in 2018. The study concludes that cryptocurrency buyers are more sensitive in 2018 to the features and quality of project each coin promotes, unlike the indiscriminating choices which dominated cryptocurrency world during the 2017 boom.
In this work, we investigate how the governance features of a managed currency (e.g., a fiat currency) can be built into a cryptocurrency in order to leverage potential benefits found in the use of blockchain technology and smart contracts. The resulting managed cryptocurrency can increase transparency and integrity, while potentially enabling the emergence of novel monetary instruments. It has similarities to cash in that it enables the general public to immediately transfer funds to a recipient without intermediary systems being involved. However, our system is account-based, unlike circulating bank notes that are self-contained. Our design would allow one to satisfy know your customer laws and be subject to law enforcement actions following legal due process (e.g., account freezing and fund seizure), while mitigating counterparty risk with checks and balances. Funds can thus be transferred only between approved and authenticated users. Our system has on-chain governance capabilities using smart contracts deployed on a dedicated, permissioned blockchain that has different sets of control mechanisms for who can read data, write data, and publish blocks. To enable the governance features, only authorized identity proofed entities can submit transactions. To enable privacy, only the block publishers can read the blockchain; the publishers maintain dedicated nodes that provide access controlled partial visibility of the blockchain data. Being permissioned, we can use a simple consensus protocol with no transaction fees. A separate security layer prevents denial of service and a balance of power mechanism prevents any small group of entities from having undue control. While permissioned, we ensure that no one entity controls the blockchain data or block publishing capability through a voting system with publicly visible election outcomes.
İnternet kullanımındaki hızlı gelişmeler ile birlikte, insan hayatına fiziksel olarak dahil olan para da dijitalleşmeye başlamıştır. Bu tür dijitalleşmiş para birimlerine genel olarak kripto para denilmektedir. Hali hazırda, Bitcoin, kripto para birimleri arasında en yüksek işlem hacmine sahiptir. İlk Bitcoin 2009 yılında piyasaya sürüldü. Fakat son birkaç yılda ciddi derecede ilgi çekmeye başladı. Bu ilginin temel nedenlerinden birisi, Bitcoin'in değerinde önemli artışların olmasıdır. Söz konusu değer artışları bağlamında, Bitcoin piyasasında spekülatif balonların varlığının araştırılması önem arz etmektedir. Bu bağlamda, çalışmanın amacı 2015-2018 dönemi boyunca Bitcoin piyasasında spekülatif balonların varlığını araştırmaktır. Amaç doğrultusunda, spekülatif balonların tespiti için Phillips vd. (2015) tarafından geliştirilen Genelleştirilmiş Eküs ADF testi kullanılmıştır. Elde edilen bulgular, Bitcoin piyasasında çok sayıda baloncuk olduğunu göstermektedir.
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.
Cryptocurrency Market today counts a market capitalization of $207 Billion, with more than 3000 coins and where main dominant Cryptos, as BTC, ETH, LTC have reached a clear popularity on Social Networks such Twitter, Facebook, Reddit and GitHub. Hence, it is today an important financial reality that attracts a lot of risk lovers and digital coin users. Nevertheless, the ambiguity of Market Nature and the huge volatility makes this market complex and approach to Cryptocurrency analysis a stiff process. It looks distant from exchange market, which appears stable and with low volatility level, and appears more similar with stock. Both in fact, present high degree of risk, but Crypto market results more fragile. All this makes price forecasting an interesting and complex game. Looking at the actual State of Art, the most interesting trend is the application of several machine learning algorithms, such as simple and multiple Linear Regression, Support Vector machine (SVM), Multilayer Perceptron (MLP) to OHCLV financial data. But the lack of seasonality and the continuous volatility drastically afflict models accuracy. Throughout the recent years, Sentiment Analysis has been involved into the Cryptocurrency price forecasting. It is a tool, based on Opinion Mining and Natural Processing Language that allows extracting polarity from Social Posts and Text, a good proxy of investor Sate of Confidence about Market. Most of works consider just Twitter sentiment and Google Trend with daily data sampling frequency. Today, few papers have inferred on Blockchain quantitative features as possible Price spread explanatory variables. Blockchain is the most underlying cryptocurrency technology and it is definable as a distributed, immutable and transparent ledger that allows emitting transactions stored by blocks. This innovation paradigm is impacting on several business areas, as Financial Transactions, Supply Chain and Politic, with a hype expectation that is touching the stars. The scope of this work, is to explore the main Cryptocurrency Sources, and evaluating which kind of data is offered, with which granularity and time horizon and in which ways (REST APIs, Web Socket APIs, csv, excel adds-on). Under this perspective, three kinds of data are stored: the OHLCV (Open, High, Low, Close, Volume) financial data, Social Data, including Facebook likes, Reddit posts, comments, GitHub activity and Blockchian data, as Block size in Byte, the number of Transactions, the Difficulty to add a new Block, the Miners Remuneration in USD. Once Data Crawling is reached, the thesis proceeds inferring on the existence of possible correlation between financial data and Social and Blockchain data. Finally, in order to empirically evaluate the validity of the work done so far, a Multilayer Perception, a Neural Network algorithm, is rune. The forecasting performances are analyzed, computing the Mean Square Error. The work counts 5 chapters, that deeper explain the above steps and with takeaways, highlighting the fundamental concepts and results, reached in each chapter. In particular the thesis is scheduled as following: - Chapter 1: Cryptocurrency Overview - Chapter 2: Blockchian as Paradigm Shift and technology - Chapter 3: Cryptocurrency Sources and Data Crawling - Chapter 4: Heatmap and variables relationship - Chapter 5: Multilayer Perceptron application
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.
Ahmed Mohamed Dahir, Fauziah Mahat, Bany‐Ariffin Amin Noordin, Nazrul Hisyam Ab Razak
Purpose Recent trends and developments in Bitcoin have led to a proliferation of studies that analyzed the Bitcoin returns and volatility; however, the volatility connectedness between Bitcoin and equity market information in emerging countries quietly remains scarce. Regarding this deficiency, the purpose of this paper is to examine the dynamic connectedness between Bitcoin and equity market information. Design/methodology/approach Daily data from January 1, 2012 to May 31, 2018 are used. The paper applies a novel time-varying parameter vector autoregression (TVP-VAR) model extended by Antonakakis and Gabauer (2017). This model addresses the biases in coefficient estimates, considering innovations from sources of time variation. Findings The findings reveal that the volatility transmission of Bitcoin return is not an important source of shocks of market returns in Brazil, Russia, India, China and South Africa (BRICS), suggesting that Bitcoin return contributes less volatility to equity market information. The results further show that Bitcoin is the main receiver of volatility while market price risk is the dominant transmission catalysts for innovations in the rest of the stock market returns. Practical implications Important implications can be derived from these findings, signaling of the demand to develop and implement volatility connectedness policy measures in order to guarantee the stability of financial assets. However, the most significant limitation lies in the fact that the analysis of this paper is restricted to the volatility connectedness between Bitcoin and equity market information in BRICS countries. Originality/value By acknowledging the wide range of econometric models, the paper uses TVP-VAR model because this methodology is a useful and relevant tool in modeling the volatility connectedness of financial variables, thus providing meaningful information to policy makers and international investors.
Abstract Cryptocurrencies are a sweltering topic in modern times of investment strategies. Since the cryptocurrency market is classified as an emerging market, in this paper a portfolio of emerging markets is compiled from the indices of four European Union (EU) countries and one cryptocurrency. The aim of this paper is to investigate how the incorporation of the Bitcoin cryptocurrency into the portfolio affects the performance of the portfolios of these countries. Moreover, by drawing an efficient frontier, the paper identifies where Bitcoin stands relative to other indices in the portfolio. The countries whose indices were used in the analysis are: Croatia, Hungary, Romania and Poland during the period from July 13, 2018 to June 07, 2019. The method used for an efficient frontier formation is Markowitz’s Modern Portfolio Theory (MPT). By applying this theory, the minimum variance portfolio at the efficient frontier was created for the portfolio with and without the cryptocurrency. The empirical analysis indicates that Bitcoin improves the effectiveness of the portfolio in emerging markets of the selected EU countries, where the expected risks of a portfolio that includes the cryptocurrency are smaller and with higher returns than those of portfolios without Bitcoin. From the Markowitz’s theory point of view, the results of the empirical analysis also indicate that Bitcoin is on the efficient frontier. Since all instruments on the efficient frontier according to the modern portfolio theory are efficient, it can be concluded that investments in such instruments depend on investor’s risk aversion.
Orăștean Ramona, Mărginean Silvia Cristina, Raluca Sava
Abstract Since 2012, there has been growing interest in bitcoin scientific research from different fields, including computer science and engineering, economics, business and finance, law and regulatory. The purpose of this paper is to evaluate bitcoin literature based on the structures and networks of science, as a first step in the research of this new phenomenon. Analysing the growing scientific literature on bitcoin published between 2012 and 2019, we provided useful insights on academic research in this field regarding publication year, type and category, authors, journals and citations. The source of the 887 documents which support the study was Web of Science Core Collection. Using VOSviewer software we have designed bibliometric maps based on text and bibliographic data. Our study provides a knowledge area map that identifies and evaluates the links between authors and countries distribution, the conceptual structure of the field, the structure and connections of most cited papers and journals. Resuming our findings, we note a concentration of the interest on some keywords (bitcoin, cryptocurrency, blockchain) and on some influential authors (with more than 100 citations per article). As a pure expression of digital economy, the research on bitcoin as an economic concept counts only 33.5% from the total contributions in the field.
This study aims to evaluate the effect of adding bitcoin in a diversified portfolio comprising traditional assets (bonds, European, Asian and international stock market indices) and alternative assets (gold and commodities) from an European investor point of view. Monthly data cover the period from August 2010 to March 2016. This period is divided into two sub-periods during the euro zone debt crisis and after the crisis. To do this, we will, first of all, apply the genetic algorithms method to optimize two types of portfolio with and without bitcoin for both subperiods. Next, we will compare the two optimal portfolios using the stochastic dominance approach during the two sub-periods. Genetic algorithms show that the weighting of bitcoin during the crisis is greater than that after the crisis, which proves that bitcoin has a safe haven value during unstable periods. The results of stochastic dominance show that during and after the crisis, the portfolio including bitcoin dominates the one without bitcoin according to the 2nd and 3rd order. This shows that risk-averse investors prefer to include bitcoin in their portfolios to maximize their expected utility.
Bitcoin’s value is highly dependent on the communities that use it. This network effect is true for all new technologies. Today’s online communities are so large in population that both the Facebook user and Youtuber populations have surpassed the Chinese population. We take a big data approach using millions of samples of posts from Twitter, Telegram, and Reddit to study how and if social media platforms, the epitome of online communities, affect Bitcoin’s price and volume as well as the price and volume of fifteen other top cryptocurrencies. We work in collaboration with Solume, a data centered fin-tech startup, as well as with Sentistrength, an opinion mining tool developed by researchers in the UK, to classify the sentiment of the millions of posts we study. We collected millions of posts related to 16 cryptocurrencies from November 2017 through August 2018 on an hourly basis and explore social media volume sentiment effect on these cryptocurrencies. Findings confirm that volumes of exchanged posts may predict the fluctuations of Bitcoin’s price but mainly, they predict volume. We also find that Reddit and Telegram posts have greater impact on Bitcoin volume than Twitter. Results indicate that information about the use of social media platforms can assist in tracking real world behavior and may even predict real financial market trends.
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.
The purpose of this article is to analyze the effect that halving has on the fair market value of bitcoins. The main hypothesis of the study is that the decline in the cost of miners’ remuneration for mining is a significant factor that affects the price of cryptocurrencies. The article examines the factors that regulate the issuing process. The significance of a limited supply of bitcoin is detailed in the article, as well as the mechanism for the implementation of the issue of new bitcoins. The study compares the historical inflation data of the US dollar and the projected data on the inflation of bitcoin. The article analyzes the main technical element of cryptocurrency – halving – when the miner’s reward is halved. This analysis includes the mathematical methods of statistical data processing. Research results show that reducing remuneration by half every four years leads to an increased market value of the cryptocurrency. This relationship is clearly illustrated by the Kendall rank correlation method. The results of the study can have a significant impact on the fundamental assessment of bitcoin and can also enable investors to assess any of the existing and operating cryptocurrencies according to this method.
With data accumulated at a rapid phase through multiple channels, algorithmic trading becomes critical in stock markets and crypto markets. In algorithmic trading, an innovative approach to integrating machine learning can provide data-driven solutions to help people invest with minimal risk and maximum returns. This study explores various machine learning techniques to model the nonlinear relationship between bitcoin prices and social sentiment data and predict the price values with some lead time. Also, the cryptocurrency market is very volatile and lacking strict governing bodies and regulators across regions making it more complex and challenging to predict the prices. Through the analysis, it is found that the sentiment data model is superior in capturing the nonlinear relationship compared to the conventional methods of technical indicators and decision trees, while the neural network models are robust and offer better accuracy in predicting bitcoin price.
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.