Blockchain Papers

Werner Kristjanpoller, Elie Bouri, Tetsuya Takaishi

No abstract is available for this record.

Nick James, Max Menzies, Jennifer Chan

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.

Jiameng Cheng, Yanke Dai

No abstract is available for this record.

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.

Qiang Ji, Elie Bouri, Ladislav Krištoufek, Brian M. Lucey

No abstract is available for this record.

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.

Karlo Ćosić, Anita Čeh Časni

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.

Hana Belhadj, Salah Ben Hamad

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.

Tiran Rothman, Chen Yakar

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.

Artur Meynkhard

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.

Yan Pang, G. Kharmega Sundararaj, Jiewen Ren

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.

Sai Srikar Nimmagadda, Pawan Sasanka Ammanamanchi

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.

David Zhao, Alessandro Rinaldo, Christopher Brookins

Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.

Élise Alfieri

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.

Azza Béjaoui, Salim Ben Sassi, Jihed Majdoub

Purpose In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors’ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies’ market but also highlights the existence of nonlinearity propriety as a stylized fact.

Eray Gemi̇ci̇, Müslüm Polat

Purpose Bitcoin has recently become the focal point of investors as a digital currency and an alternative payment method. Despite Bitcoin being in the spotlight, a gap in the literature on its price-setting behaviors has been observed. This study aims to contribute to the literature by investigating the relationship between Bitcoin price and volume in the period between January 1, 2012 and April 7, 2018 through a symmetric and asymmetric causality test. Design/methodology/approach Daily price and volume data relevant to Bitcoin traded in the Bitstamp market were obtained from www.bitcoincharts.com . Within the framework of data applicable for analysis, the data set for this study includes a total of 2,286 observations for the period between January 1, 2012 and April 7, 2018. Findings Based on the results of the standard causality test, a causality relationship was determined from price to volume. Based on the results of the asymmetric causality test between positive and negative shocks of variables, a unilateral causality relationship was determined from negative shocks in Bitcoin prices to negative shocks in trading volume as well as from positive shocks in trading volume to positive shocks in prices. Furthermore, it was found that the relationship between Bitcoin price and volume is cointegrated. Practical implications The empirical results can be used by investors and portfolio managers to make trading decisions. Originality/value The contribution of this paper to the literature is that it is the first study on the symmetric and asymmetric causality relationship between Bitcoin price and volume. Moreover, this paper reveals short- and long-term behaviors of Bitcoin using the cointegration test used for determining the long-term relationship between Bitcoin price and volume.

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.

Jędrzej Białkowski

No abstract is available for this record.

Michaela Hönig

No abstract is available for this record.

Νikolaos Kyriazis

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.

Abdullah Yalaman

No abstract is available for this record.

Alessandra Cretarola, Gianna Figà‐Talamanca

No abstract is available for this record.