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Blockchain and cryptocurrencies: economic and financial research
Abstract
The motivation of proposing and editing the Special Issue “Blockchain and cryptocurrencies” came from the inspirational invited and contributed talks at the 43rd annual A.M.A.S.E.S. conference held in Perugia in September 2019. All the papers have gone through the journal regular refereeing process under the same standards set by the journal, and nine contributions were finally accepted for publication. © 2021, The Author(s), under exclusive licence to Associazione per la Matematica Applicata alle Scienze Economiche e Sociali (AMASES).
... To understand the blockchain token environment from a financing perspective it is necessary to differentiate the multitude of options, including the security tokens, which unlike utility tokens provide the investor with revenues, such as dividends that are part of the profit [51]. Chod et al. [34] prove that capital is raised through token issuances due to the payment being carried out after transaction volume rather than while still under-provisioning profit. ...
... White paper quality Confidence, credibility, transparency - [50,52] Issuance country Positive or negative depending on the country's policies [52] Limited total token supply Scarcity, value appreciation, incentives Accessibility and liquidity [50,52] Bonus scheme post-ICO Attracting new participants and rewarding the early ones Dilution or short-term speculation [31,52,54] Token Distribution Stakeholders' participation, decentralization Price volatility and lack of control [31,51,52] ICO Duration Accessibility, increased investor participation Fluctuations, uncertainty [46,52,54] Unregulated environment Innovation, financial inclusion Manipulation, uncertainty, frauds [46,49,53] Investor voting rights Transparency and consensus-building Complex decision-making process [46,51] Target amount Measurable success, confidence Missed target [46,47,51] GitHub presence Development progress, code quality Inactivity [46] Price-cost ratio Profitability Market acceptance [47,49] Number of tokens sold Market perception, liquidity Investor expectation [47,49,51] ...
... White paper quality Confidence, credibility, transparency - [50,52] Issuance country Positive or negative depending on the country's policies [52] Limited total token supply Scarcity, value appreciation, incentives Accessibility and liquidity [50,52] Bonus scheme post-ICO Attracting new participants and rewarding the early ones Dilution or short-term speculation [31,52,54] Token Distribution Stakeholders' participation, decentralization Price volatility and lack of control [31,51,52] ICO Duration Accessibility, increased investor participation Fluctuations, uncertainty [46,52,54] Unregulated environment Innovation, financial inclusion Manipulation, uncertainty, frauds [46,49,53] Investor voting rights Transparency and consensus-building Complex decision-making process [46,51] Target amount Measurable success, confidence Missed target [46,47,51] GitHub presence Development progress, code quality Inactivity [46] Price-cost ratio Profitability Market acceptance [47,49] Number of tokens sold Market perception, liquidity Investor expectation [47,49,51] ...
Blockchain and tokens are relatively new research areas insufficiently explored from both technical and economic perspectives. Even though tokens provide benefits such as easier market access, increased liquidity, lower transaction costs, and automated transactional process, their valuation and price determination are still challenging due to factors such as a lack of intrinsic value, volatility, and regulation making trading risky. In this paper, we address this knowledge gap by reviewing the existing literature on token creation and valuation to identify and document the factors affecting their valuation, investment, and founding, as well as the most promising domains of applicability. The study follows the PRISMA methodology and uses the Web of Science database, defining clear research questions and objective inclusion criteria for the articles. We discuss token technical development, including creating, issuing, and managing tokens on an Ethereum blockchain using smart contracts. The study revealed several key factors that significantly impact the field of tokenomics: demand and supply, social incentives, market conditions, macroeconomics, collective behavior, speculation, and inclusion in index funds. The most relevant use cases of blockchain and tokens are related to the digitization of virtual and physical assets, accountability, and traceability usual in smart grids or supply chains management, social governance, and art and gamification including metaverse.
... 3 Dapat diketahui sifat cryptocurrency yang terdesentralisasi dan anonim (atau nama samaran) telah memungkinkan dan memfasilitasi penjahat untuk terlibat dalam sejumlah aktivitas ilegal seperti pencucian uang, pendanaan terorisme, dan banyak lainnya. 4 Cryptocurrency bisa turun drastis karena penipuan yang terungkap atau dugaan peretasan dan masalah tersembunyi lainnya. Misalnya, pada bulan Juni 2019, harga bitcoin kehilangan lebih dari 10% nilainya dalam beberapa menit karena crash dan pemadaman pertukaran digital Coinbase. ...
... 3 Ibid. 4 Ibid. 5 Giancarlo Giudici, Alistair Milne, and Dmitri Vinogradov, "Cryptocurrencies: Market Analysis and Perspectives," Journal of Industrial and layanan tersebut. 6 Dari keterangan di atas penulis akan menganalisis bagaimana cryptocurrency legal di banyak negara, terlepas dari kenyataan bahwa tidak ada satu negara pun yang menyediakan kerangka hukum untuk itu dan tidak ada pemerintah yang dapat dimintai pertanggungjawaban atas segala kerugian yang diderita oleh penggunanya. ...
Cryptocurrency is the first application of blockchain technology, but the potential of this technology is not limited to the monetary system and its widespread adoption is still a worldwide topic causing many countries to hide this cryptocurrency. Here the author tries to assess crypto assets based on studies of fikih and fatwa abroad. In this study the authors used sociological legal research, this research method serves to see the law in a real sense and examine how the law works in society. To analyze existing theories, this study uses a conceptual approach to analyze existing theories so that solutions to legal problems can be found. The results in this study are that there are two thoughts, the first school of thought argues that cryptocurrencies can be used for payments, remittances, and storing assets as long as users know in detail the types of cryptocurrencies, the second opinion is prohibited in the eyes of Sharia because of excessive risks for individuals, countries and no clear laws. conclusions in this study. In the view of fikih scholars who allow it to refer to the understanding that fluctuations in crypto are not classified as gharar, especially for people who have cryptocurrency knowledge. from the results of the fatwa analysis of the permissibility of cryptocurrency, namely in Malaysia, LBM PWNU Yogyakarta Indonesia and in China in accordance with the fatwa and legal provisions, while those that prohibit it are in Middle Eastern countries and LBM PWNU East Java Indonesia.Keywords: Cryptocurrency, Fikih and Fatwa Studies
... However, in the context of this type of application, we are confronted with an important problem-the confidentiality of the applications. To overcome this problem, several possibilities are available to secure the data, such as data encryption, elimination of personal information, etc. [1,2]. In this work, we will propose an approach based on Blockchain technology to secure information. ...
... − For the transfer of assets (money, securities, shares, etc.) [2] − For a supply chain and a better traceability of assets and products [3] − For securing confidential data (voting, health, diplomas, etc.) [4][5][6][7]. ...
With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of patient’s health. To meet the constraint of medical secrecy and confidentiality of information, we will use the Blockchain as a secure system. Our system will use the data collected by several smart sensors such as blood pressure, SPO2 concentration, and EEG signals. These encrypted data will be collected by an embedded Raspberry PI 4 platform (working as a smart data relay) before being processed (on a backend server) and then saved in an embedded Blockchain node. The preliminary results show the effectiveness of the proposed platform as a candidate of a low-cost example of secured Electronic Health Record (EHR).
... December 2017 (Matkovskyy & Jalan, 2019;Sami & Abdallah, 2021). This discovery led to a more indepth investigation into the hedging and diversification properties of Bitcoin compared to traditional financial assets (Urquhart & Zhang, 2019;Guesmi et al., 2018;Bouri et al., 2017;Cretarola et al., 2021). Other authors suggest that the assets become more correlated during economic downturns. ...
The COVID-19 pandemic has challenged the notion that cryptocurrencies are uncorrelated with traditional asset markets. This study uses VAR-OLS techniques to investigate the time-varying correlation between Bitcoin and three major European stock market indices from January 4, 2016, to February 26, 2021. Our results show that cryptocurrencies and stock markets are dependent during crisis periods, but not during non-crisis periods. This confirms the time-varying correlation between cryptocurrencies and stock markets, which depends on the extent and persistence of responses to own and cross shocks. To improve the robustness of our results, we also test the impact of government measures on Bitcoin and stock market indices and find that they are both affected by these measures. Our study adds to the literature by examining the impacts of pandemics on the correlations between Bitcoin returns and the stock market, oil, and gold index returns, which have so far been unaddressed.
... Thus, investigating each blockchain project as an independent system can alleviate information asymmetry. However, the interworking barriers between blockchain systems limit their application space, resulting in many "information islands" 18 . In traditional financial markets, the connectivity of human resources, commodities, and capital plays a vital role in expanding market boundaries, improving the efficiency of resource allocation, and enhancing market effectiveness 19,20 . ...
With the rapid development of blockchain technology, the financial and monetary (FM) blockchain fields also began to collide. Therefore, this study aims to explore the relationship between the two fields and efficiently evaluate the security of financial information. Firstly, this study introduces the theoretical basis of blockchain in the dynamic linkage mechanism of FM and gives the overall framework of digital currency based on blockchain. Meanwhile, the relationship between blockchain and digital finance is empirically analyzed and designed. Secondly, the framework of financial information service security assessment (ISSA) is created using blockchain technology, and the frame of security risk evaluation is verified by taking electronic invoice information as the research object. Finally, the results show that: (1) foreign exchange (forex) events and major stock index decline events have a significant impact on the return of Bitcoin (BTC) in the short term. Moreover, more than 70% of uncertainty events will make BTC’s abnormal return (AR) significantly positive; (2) Under the influence of forex uncertainty events, only one BTC’s AR is remarkably negative in short order, while the other seven times are markedly positive. In the uncertainty events of major stock indexes, only two times are significantly negative, and the other six times are positive. This indicates that uncertain events in the short run make prominent AR of BTC; the proposed blockchain-based ISSA model and assessment index are scientific, feasible, and operable.
... Cretarola and others systematically studied the supervision mode of world commercial banks and its relationship with financial development. However, the effectiveness of various financial supervision theories has not been systematically verified in the existing literature [18]. Egelund Muller and others believe that the public interest theory lacks empirical support, and the reason why many industries are regulated is not because of the higher degree of monopoly or easier to trigger monopoly [19]. ...
Decentralization, stability, security, and immutability are all features of blockchain technology. Blockchain, as the underlying technology of Bitcoin’s digital monetary system, is currently sweeping the globe. Blockchain is a revolutionary decentralized database technology that employs encryption, a timestamp chain data structure, a distributed consensus mechanism, and other technologies to achieve decentralization, tamper resistance, easy tracking, and programmable smart contracts. In the face of rising financial technology, we must maintain inclusive, technological, and invasive regulatory principles that not only foster financial innovation, but also conduct dynamic supervision to avoid systemic financial hazards. The consensus algorithm is one of the main blockchain technologies that has a direct impact on the system’s functioning. As a result, in this paper, we propose a blockchain-based development and supervision method for financial technology, as well as an application of this technology to commercial settlement, which can significantly reduce data complexity, time consumption, and the structural chain phenomenon in existing transaction settlement. We bring the idea of pow competition into DPoS, construct a consensus algorithm with an upgrade mechanism, and call it delegated proof of work, based on an in-depth investigation of the working principle of pow (proof of work) (dDPoS). The blocking efficiency of the dDPoS consensus method is around one block every 10 seconds, which is significantly higher than the blocking efficiency of the POW and POS consensus algorithms. As a result, it offers a potential answer to traditional centralized institutions’ concerns of high brokerage costs and insecure central storage, as well as a wide range of application possibilities.
Block Chain Cryptocurrencies are playing vital role in Financial as well in all other sectors .Recently in the year 2022 battle between Russo-Ukrainian is in fact enduring battle between two countries Russia and Ukraine. Subsequent to the Russian military build-up on the Russia–Ukraine border from late 2021, the battle extended ominously when Russia propelled a complete incursion of Ukraine on 24 February 2022.Monetary problem obviously showcases a foremost role in wars, the 2022 war between Russia and Ukraine is the prime major battle with a major but role of crypto-currencies. Because Russian military forces attacked Ukraine the United States along with its partners have imposed exceptional sanctions on Russia. These situations have led to lot of queries, regarding whether crypto-currencies can be employed by Russian performers to circumvent the authorizations. In a broader sense, the Russia-Ukraine crisis has made the policymakers to resolve how to normalize digital possessions. This chapter emphasizes on how best Ukraine is able to manage the financial crisis during Ukraine –Russia war using crypto-currencies and Non-fungible tokens in terms of Military and humanity.
The field of modern cryptography faces significant standardization and regulatory challenges due to the rapid advancement of technology and the increasing importance of secure communication in various sectors. This abstract delves into the complexities surrounding the standardization and regulation of cryptographic algorithms and protocols.Standardization plays a crucial role in ensuring interoperability, security, and trustworthiness in cryptographic systems. However, the process of developing and implementing cryptographic standards is fraught with challenges such as balancing security requirements with usability, addressing evolving threats, and accommodating diverse stakeholder interests. One of the primary challenges in cryptographic standardization is the tension between the need for transparency and the desire to protect sensitive information. Openness and transparency are essential for building trust and verifying the security properties of cryptographic algorithms and protocols. However, excessive transparency can also expose vulnerabilities and aid adversaries in exploiting weaknesses. Regulatory challenges further complicate the landscape of modern cryptography. Governments and regulatory bodies seek to balance the interests of national security, law enforcement, and individual privacy rights. This often leads to debates over encryption backdoors, key escrow mechanisms, and export controls on cryptographic technologies. Navigating these challenges requires collaboration between cryptographic experts, policymakers, industry stakeholders, and civil society organizations. It involves striking a delicate balance between security, privacy, and regulatory compliance while fostering innovation and advancing the state-of-the-art in cryptography.In conclusion, standardization and regulatory challenges in modern cryptography highlight the intricate interplay between security, technology, and policy. By addressing these challenges through multi-stakeholder collaboration, the cryptographic community can promote the development and adoption of robust cryptographic standards that enhance security and privacy in the digital age.
The integration of machine learning (ML) algorithms in Anti-Money Laundering (AML) practices has garnered significant attention due to its potential to enhance the detection and prevention of illicit activities in the cryptocurrency ecosystem. This systematic literature review analysed the effectiveness of integrating ML algorithms in detecting and preventing crypto laundering activities, identify the most frequently used ML algorithms, examine trends in publication and research methodologies, and discuss key challenges and constraints associated with integrating ML technologies into AML frameworks. A comprehensive search strategy was employed to identify relevant studies, resulting in the inclusion of 52 articles published between 2019 and 2023. The findings reveal a growing interest in the field, with a notable increase in publications in recent years. Traditional ML models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) remain prevalent, while deep learning models like Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks are gaining popularity. Graph Convolutional Networks (GCNs) have emerged as a significant area of exploration, particularly in the context of graph data analysis in cryptocurrencies. Despite advancements in ML, cryptocurrencies continue to pose a high risk of money laundering due to the practical challenge of implementation ownership of the various ML models. Future research should focus on how these challenges will be addressed to ensure the effective and sustainable use of ML technologies in real-world AML practices.
By the end of 2021, the worldwide cryptocurrency market valuation had hit a whole high of $3 trillion. Blockchain technology underpins cryptocurrencies such as Bitcoin and Ethereum. The adoption of blockchain, as well as the technology and products it enables, will continue to have a significant influence on company operations. However, blockchain technology is much more than a secure cryptocurrency transfer method. It may be utilised in areas other than finance, including as healthcare, insurance, voting, welfare benefits, gaming and artist royalties. The global economy is prepared for the blockchain revolution, with the technology already having an influence on business and society on many levels. If the term “revolution” seems extreme, consider that eight of the world’s ten largest corporations are developing a variety of blockchain-based solutions. Any enterprise or organisation that is engaged in the recording and oversight of any type of transaction stands to profit from shifting its operations to a blockchain-based platform. This paper discusses about the various roles blockchain has taken up over the years in several different industries along with the future opportunities and scope of expansion to numerous other professional sectors.
Normal cash has developed and appears numerous downsides such as inaccessibility. It is inclined to burglary and is intensely directed by government offices. Cryptocurrencies have risen as a egotistic money related framework. They depend upon secure disseminated ledger data structure. Mining plays a critical portion in this framework [1]. Basically, our cryptocurrency could be a conveyed database that keeps up tamper-proof information structure pieces containing his bunches of person exchanges. Blockchain innovation can be a widely emerging approach to data innovations. Bitcoin as a cryptocurrency has made several considerations since it was one of its earliest implementations. They discuss the key elements driving the development of sophisticated cryptocurrencies alongside Ethereum, a blockchain implementation with a focus on informed contracts [1]. In its most basic form, our cryptocurrency may be thought of as a distributed database that keeps track of tamper-proof data structure blocks comprising batchesof individual transactions [1].
Recently, Indian Finance minister Nirmala Sitharaman announced in Union budget 2022-23 that Indian government will put 30% tax (the highest tax slab in India) on income generated from cryptocurrencies. Big financial institutions, experts and academicians have different opinions in this regard. They claim that it would be the end of cryptocurrency market in India or it would be possible that RBI (Reserve Bank of India) may launch its own crypto or digital currency. So in this context, in this article, the journey and future aspects of cryptocurrency in India are discussed and we hope that it will be a reference for further research and discussion in this area.
We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.
Initial coin offerings (ICOs) represent a novel funding mechanism where digital tokens are issued on the blockchain and sold to investors. One major reason for the success of this financing model is the fact that the issued tokens can immediately be traded on secondary markets. This event study analyzes 250 exchange cross-listings of 135 different tokens issued through ICOs on 22 cryptocurrency exchanges. We find significant abnormal returns of 6.51% on the listing day and 9.97% over a seven-day window around the event. Further analysis shows that the results clearly differ for individual cryptocurrency exchanges, as listings on individual exchanges yield returns of up to 34% on the event day, while others are negligible. An investigation of liquidity-related metrics shows that lower prior trading volume and asset market capitalization have positive effect on listing returns. Investors use phases of high market liquidity to sell off positions around the period of cross-listing events. The results on the cross-listing effects of ICOs may be of relevance to investors/traders, ICO projects, cryptocurrency exchanges and regulators.
In this paper, we come up with an original trading strategy on Bitcoins. The methodology we propose is profit-oriented , and it is based on buying or selling the so-called Contracts for Difference, so that the investor’s gain, assessed at a given future time t , is obtained as the difference between the predicted Bitcoin price and an apt threshold. Starting from some empirical findings, and passing through the specification of a suitable theoretical model for the Bitcoin price process, we are able to provide possible investment scenarios, thanks to the use of a Recurrent Neural Network with a Long Short-Term Memory for predicting purposes.
In this study, we characterized the dynamics and analyzed the degree of synchronization of the time series of daily closing prices and volumes in US$ of three cryptocurrencies, Bitcoin, Ethereum, and Litecoin, over the period September 1,2015–March 31, 2020. Time series were first mapped into a complex network by the horizontal visibility algorithm in order to revel the structure of their temporal characters and dynamics. Then, the synchrony of the time series was investigated to determine the possibility that the cryptocurrencies under study co-bubble simultaneously. Findings reveal similar complex structures for the three virtual currencies in terms of number and internal composition of communities. To the aim of our analysis, such result proves that price and volume dynamics of the cryptocurrencies were characterized by cyclical patterns of similar wavelength and amplitude over the time period considered. Yet, the value of the slope parameter associated with the exponential distributions fitted to the data suggests a higher stability and predictability for Bitcoin and Litecoin than for Ethereum. The study of synchrony between the time series investigated displayed a different degree of synchronization between the three cryptocurrencies before and after a collapse event. These results could be of interest for investors who might prefer to switch from one cryptocurrency to another to exploit the potential opportunities of profit generated by the dynamics of price and volumes in the market of virtual currencies.
In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.
Whereas much research has largely investigated the safe haven, diversifier and hedge proprieties of cryptocurrency, very few papers have analyzed the hedging issue of cryptocurrency with other assets. As such, this paper attempts to investigate the possibility if Bitcoin can be hedged by selected fiat currencies (EUR, JPY and GBP) as Bitcoin prices have experienced high and persistent volatility. To do so, we compute optimal hedge ratios between Bitcoin and fiat currencies over the period 02/02/2012–30/11/2017 based on the VAR-DCC-GARCH model, VAR-ADCC-GARCH model and VAR-component GARCH-DCC model. A rolling window analysis is employed to establish out-of-sample one-step-ahead forecasts of dynamic conditional correlations between different assets. This leads to establish time-varying hedge ratios and thus dynamic cross-hedging Bitcoin/fiat currency markets. The empirical results clearly show the time-varying correlations between Bitcoin and fiat currencies under different specifications, implying a dynamic behavior of the relationship between such assets. For all the proposed models, such dynamic correlations are rather characterized by trending downward over the period under study. The results also display time-varying hedge ratios which lead to an ongoing regular demand for rebalancing the hedged positions under different specifications. As a matter of fact, using various models which take into account different aspects of volatility and correlation structures allows to better implement dynamic hedging strategies.
This paper analyzes the relationships between volatilities of five cryptocurrencies, American indices (S&P500, Nasdaq, and VIX), oil, and gold. The results of the BEKK-GARCH model show evidence of a higher volatility spillover between cryptocurrencies and lower volatility spillover between cryptocurrencies and financial assets. The results of the DCC-GARCH model identify an important effect of the launch of Bitcoin futures. During the stability period, the overarching implications of the results are that there is a persistence of correlation between cryptocurrencies in high positive value and low dynamic conditional correlations between cryptocurrencies and financial assets. Also, we find that Bitcoin and gold are considered hedges for the US investors before the coronavirus crisis. Our results show that cryptocurrencies may offer diversification benefits for investors and are diversifiers during the stability period. At the beginning of 2020, we observe that the conditional correlation increased between cryptocurrencies, stock indexes, and oil which confirm the effect of the coronavirus contagion between them. Unlike gold, digital assets are not a safe haven for US investors during the coronavirus crisis.
Empirical evidence suggests the presence of bubble effects on Bitcoin price dynamics during its lifetime, starting in 2009. Previous research, mostly empirical, focused on statistical tests in order to detect a bubble behavior at some point in time. Few exceptions suggested specific time series models capable to describe such phenomena. We contribute this stream of literature by considering a continuous time stochastic model for Bitcoin dynamics, depending on a market attention factor, which is proven to boost in a bubble under suitable conditions. Here, we define a bubble following the theory of mathematical bubbles introduced by Philip E. Protter and coauthors. Specifically, we prove that the presence of a bubble is related to the correlation between the market attention factor on Bitcoin and Bitcoin returns being above a threshold, i.e. when marked attention affects Bitcoin prices and converse, creating a vicious loop. This phenomenon has been labelled market exuberance by Robert J. Shiller, recipient of the 2013 Nobel prize in Economic Sciences. The model is fitted on historical data of Bitcoin prices, by considering either the total trading volume or the Google Search Volume Index as proxies for the attention measure. According to our numerical results, a bubble effect is evidenced in the early years of Bitcoin introduction, namely 2012–2013, as well as in the recent race of 2017.
We study the dynamics of volatility spillover across four major cryptocurrency returns namely Bitcoin, Ethereum, Ripple and Litecoin from 15−08−2015 to 18−01−2018. In the first step, an IGARCH (1,1)-DCC (1,1) multivariate GARCH model is estimated to quantify the nature of volatility spillovers. From GARCH results, it is seen that there is statistically significant volatility spillover from Bitcoin to Ethereum and Litecoin during the period of analysis. The conditional correlation measures point towards the possibility of moderate return co-movement among the crypto-currency returns. The conditional covariance measures show negligible volatility spillover during the initial periods and provide evidence towards increased volatility spillover after 2017. Wavelet coherence measures shows evidence towards correlation among the crypto-currencies to be persistent across the short run, while the pairwise wavelet cross-spectral analysis confirms the findings obtained from conditional covariance measures. It is found that other crypto-currencies are influenced by fluctuations in bitcoin prices. Overall, the results indicate the possibility of turbulence in the crypto-currency markets and point towards the possibility of herding behaviour in crypto-currency markets.
Bitcoin is a digital currency started in early 2009 by its inventor under the pseudonym of Satoshi Nakamoto. In the last few years, Bitcoin has received much attention and has shown a surprising price increase. Bitcoin is currently traded on many web-exchanges making it a rare example of a good for which different prices are readily available; this feature implies important issues about arbitrage opportunities since prices on different exchanges are shown to be driven by the same risk factor. In this paper, we show that simple strategies of strong arbitrage arise by trading across different Bitcoin exchanges taking advantage of the common risk factor. The suggested arbitrage strategies are based on two alternative model specifications. Precisely, we consider the multivariate versions of Black and Scholes model and of an attention-based dynamics recently introduced in the literature.
We are interested in describing the returns and volatility dynamics of major cryptocurrencies. Very high volatility, large abrupt price swings, and apparent long memory in volatility are documented features of such assets. We estimate a multivariate stochastic volatility model with discontinuous jumps to mean returns and volatility. This formulation allows us to extract a time-varying shared average volatility and to account for possible large outliers. Nine cryptocurrencies with roughly three years of daily price observations are considered in the sample. Our results point to two high volatility periods in 2017 and early 2018. Qualitatively, the permanent volatility component seems driven by major market developments, as well as the level of popular interest in cryptocurrencies. Transitory mean jumps become larger and more frequent starting from early 2017, further suggesting shifts in cryp-tocurrencies return dynamics. Calibrated simulation exercises suggest the long memory dependence features of cryptocurrencies are well reproduced by stationary models with jump components.
Motivated by the emergence of Bitcoin as a speculative financial investment, the purpose of this paper is to examine the persistence in the level and volatility of Bitcoin price, accounting for the impact of structural breaks. Using parametric and semiparametric techniques, we find strong evidence in favour of a permanency of the shocks and lack of mean reversion in the level series. We also reveal evidence of structural changes in the dynamics of Bitcoin. After accounting for the structural breaks in the level series, evidence of mean reversion is uncovered in some cases. Further analyses show evidence of a long memory in the two measures of volatility (absolute and the squared returns), whereas some cases of short memory are revealed in the squared returns series in particular. Practical implications are discussed on the inefficiency in the Bitcoin market and its importance for Bitcoin users and investors.
This paper contributes to the literature on cryptocurrencies by examining the performance of naïve (1/N) and optimal (Markowitz) diversification in a portfolio of four popular cryptocurrencies. We employ weekly data with weekly rebalancing and show there is very little to select between naïve diversification and optimal diversification. Our results hold for different levels of risk-aversion and an alternative estimation window.
Bitcoin is defined as digital money within a decentralized peer-to-peer payment network. It is a hybrid between fiat currency and commodity currency without intrinsic value and independent of any government or monetary authority. This paper analyses the question of whether Bitcoin is a medium of exchange or an asset and more specifically, what is its current usage and what usage will prevail in the future given its characteristics. We analyse the statistical properties of Bitcoin and find that it is uncorrelated with traditional asset classes such as stocks, bonds and commodities both in normal times and in periods of financial turmoil. The analysis of transaction data of Bitcoin accounts shows that Bitcoins are mainly used as a speculative investment and not as an alternative currency and medium of exchange.
This study empirically examines interdependencies between BitCoin and altcoin markets in the short- and long-run. We apply time-series analytical mechanisms to daily data of 17 virtual currencies (BitCoin + 16 alternative virtual currencies) and two Altcoin price indices for the period 2013-2016. Our empirical findings confirm that indeed BitCoin and Altcoin markets are interdependent. The BitCoin-Altcoin price relationship is significantly stronger in the short-run than in the long-run. We cannot fully confirm the hypothesis that the BitCoin price relationship is stronger with those Altcoins that are more similar in their price formation mechanism to BitCoin. In the long-run, macro-financial indicators determine the altcoin price formation to a greater degree than BitCoin does. The virtual currency supply is exogenous and therefore plays only a limited role in the price formation.
We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short-term positive impact on market activity and illiquidity and see a persistent change in the price.
We propose an optimal dynamic pairs trading strategy model for a portfolio of cointegrated assets. Using stochastic control techniques, we compute analytically the optimal portfolio weights and relate our result to several other strategies commonly used by practitioners, including the static double-threshold strategy. Finally, we apply our model to a bitcoin portfolio and conduct an out-of-sample test with historical data from three exchanges, with two cointegrating relations.
This chapter considers the evolution of alternative digital currencies from DigiCash to Bitcoin, the very digital currency that has spurred the development of this book. Specifically, this chapter explores the general features of Bitcoin and its benefits and risks before suggesting some conditions for a successful cryptocurrency to thrive. The chapter then concludes with optimism about the future of cryptocurrency and its underlying technology.
This paper sets out to explore the nexus between cryptocurrencies connected to cannabis production and the three highest capitalization digital currencies. Daily data are employed that cover the period 26 October 2017–3 January 2020. Generalized autoregressive schemes in the form of GARCH, EGARCH, TGARCH and GJR-GARCH are adopted in order to study volatility characteristics. Findings reveal that GARCH and GJR-GARCH specifications are most appropriate in the majority of cases. This reveals the existence of thresholds in the volatility of cannabis cryptocurrencies when examining their nexus with major digital currencies. This renders them riskier but also more attractive to speculators. Thereby, they abide by the overall character of such innovative forms of liquidity and investment. Overall, evidence indicates that Bitcoin presents medium to strong positive linkage with cannabis cryptocurrencies while Ripple is the most weakly connected to them. Thereby, none of the major digital currencies under scrutiny can serve as efficient hedger against cannabis cryptocurrencies.
Since Bitcoin’s introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins and tokens. In this paper, we analyze the dynamics of this burgeoning industry. We consider both cryptocurrency coins and tokens. The paper examines the dynamics of coin and token creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins and tokens are abandoned and subsequently “resurrected”. We also study trading activity. Our data spans more than 4 years: there are 1082 coins and 725 tokens in the data. While there are some similarities between coins and tokens regarding dynamics, there are some striking differences as well. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. Tokens experience abandonment less frequently, with only 7% abandonment and 5% permanent token abandonment at the end of the data. Using linear regressions, we find that market variables such as the bitcoin price are not associated with the rate of introducing new coins, though they are positively associated with issuing new tokens. We find that for both coins and tokens, market variables are positively associated with resurrection. We then examine the effect that the bursting of the Bitcoin bubble in December 2017 had on the dynamics in the industry. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the bursting of this bubble.
In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies.
Nakamoto doublespend strategy, described in Bitcoin foundational article, leads to total ruin with positive probability. The simplest strategy that avoids this risk incorporates a stopping threshold when success is unlikely. We compute the exact profitability and the minimal double spend that is profitable for this strategy. For a given amount of the transaction, we determine the minimal number of confirmations to be requested by the recipient that makes the double-spend strategy non-profitable. This number of confirmations is only 1 or 2 for average transactions and for a small relative hashrate of the attacker. This is substantially lower than the original Nakamoto number, which is about six confirmations and is widely used. Nakamoto analysis is only based on the success probability of the attack instead of on a profitability analysis that we carry out.
This paper aims to study the pricing of Bitcoin options with a view to incorporating both conditional heteroscedasticity and regime switching in Bitcoin returns. Specifically, a nonlinear time series model combining both the self-exciting threshold autoregressive (SETAR) model and the generalized autoregressive conditional heteroscedastic (GARCH) model is adopted for modeling Bitcoin return dynamics. Specifically, the SETAR model is used to model regime switching and the Heston-Nandi GARCH model is adopted to model conditional heteroscedasticity. Both the conditional Esscher transform and the variance-dependent pricing kernel are used to specify pricing kernels. Numerical studies on the Bitcoin option prices using real bitcoins data are presented.
We investigate arbitrage at four decentralized bitcoin exchanges that contribute to calculation of the index serving as the underlying price for the CME bitcoin futures. Deviations from price parity are much higher on average, more volatile, exhibit persistency and occasionally reach fairly large extremes during the 2016-2017 period, becoming economically small, much less volatile and sporadic afterwards. We design an arbitrage investment strategy based on the premise of convergence to parity and account for transaction costs. Profitable arbitrage opportunities have become sparse and scarce since 2018.
In this paper we extend the model in Cretarola, Figà-Talamanca, “Detecting bubbles in Bitcoin price dynamics via market exuberance”, Annals of Operations Research (2019), by allowing for a state-dependent correlation parameter between asset returns and market attention. We assume that the change of state is described by a continuous time latent Markov chain and we propose an estimation procedure based on the conditional maximum likelihood and on the Hamilton filter. Finally, model parameters, as well as Markov chain transition probabilities, are estimated on Bitcoin and Ethereum returns in case market attention is measured via the Google Search Volume Index for the keywords “bitcoin” and “ethereum”, respectively; up to four regimes are considered in the empirical application. The empirical outcomes show that the model is not only capable of identifying bubble and non-bubble regimes but also enables the interpretation of the correlation between cryptocurrencies and their market attention as a tuning to define the speed at which a bubble boosts.
This study examines the dependence and contagion risk between Bitcoin (BTC), Litecoin (LTC) and Ripple (XRP) using non-parametric mixture copulas (developed by Zimmer, 2012) and recently proposed methods of full-range tail dependence copulas (advanced by Hua, 2017, Su and Hua, 2017), for the period from 04-08-2013 to 17-06-2018. The Chi-plots and Kendall plots results show heavy tail dependence between each pairs of the cryptocurrencies. Evidence from the mixture copula indicates that for the BTC-LTC pair the upper-tail dependence is both stronger and more prevalent, while for the other pairs of cryptocurrencies the lower-tail dependence is very strong and more prevalent. However, the results of the full-range tail dependence copulas reveal a strong and prevalent upper and lower-tail dependence of each pairs of cryptocurrencies. These results provide evidence of significant risk contagion among price returns of major cryptocurrencies, both in bull and bear markets.
In the last few years Bitcoin price dynamics has been the subject of intense research. One of the main stream of investigation is the identification of relevant factors affecting its returns and volatility; empirical evidence suggests a positive association between returns and sentiment proxies about the Bitcoin network, such as Wikipedia inquiries, internet search intensity on the topic, trading volume in main exchanges or sentiment measures obtained via natural language processing algorithms applied on specialized forums comments or social media posts on the theme. In this paper we investigate the association of trading volume and internet search intensity with Bitcoin returns and volatility, complementing the outcomes in Figá-Talamanca and Patacca (Decis Econ Fin ISSN: 1129-6569, https://doi.org/10.1007/s10203-019-00258-7, 2019) and Urquhart (Econ Lett 166:40–44, ISSN: 0165-1765, https://doi.org/10.1016/j.econlet.2018.02.017, 2018): we find no direct relationship between the two market attention measures and returns while both the trading volume and the internet search intensity affect positively Bitcoin volatility. Conversely, an increase in Bitcoin returns does increase both trading volume and internet search intensity, evidencing an inverse relationship between returns and attention measures. As a byproduct, we also detect a positive association between trading volume and the internet search intensity and no reverse relationship. Since market attention, especially internet search volume, do increase around relevant events and corresponding news or announcements for the Bitcoin market, we also analyze whether and to which extent the above relationships change, after specific events are taken into account. Indeed, by applying two different approaches, we show that the relationships may change significantly.
The goal of this paper is to provide a novel quantitative framework to describe the Bitcoin price behavior, estimate model parameters and study the pricing problem for Bitcoin derivatives. To this end, we propose a continuous time model for Bitcoin price motivated by the findings in recent literature on Bitcoin, showing that price changes are affected by sentiment and attention of investors, see e.g., (Kristoufek in Sci Rep 3:3415, 2013, PLoS ONE 10(4):e0123923, 2015; Bukovina and Marticek in Sentiment and bitcoin volatility. Technical report, Mendel University in Brno, Faculty of Business and Economics 2016). Economic studies, such as Yermack (Handbook of Digital Currency, chapter second. Elsevier, Amsterdam, pp 31–43, 2015), have also classified Bitcoin as a speculative asset rather than a currency due to its high volatility. Building on these outcomes, the price dynamics in our suggestion is indeed affected by an exogenous factor which represents market attention in the Bitcoin system. We prove the model to be arbitrage-free under a mild condition and we fit the model to historical data for the Bitcoin price; after obtaining a approximate formula for the likelihood, parameter values are estimated by means of the profile likelihood method. In addition, we derive a closed pricing formula for European-style derivatives on Bitcoin, the performance of which is assessed on a panel of market prices for Plain Vanilla options quoted on www.deribit.com.
In this paper, we analyze the relative impact of attention measures either on the mean or on the variance of Bitcoin returns by fitting nonlinear econometric models to historical data: Two non-overlapping subsamples are considered from January 1, 2012, to December 31, 2017. Outcomes confirm that market attention has an impact on Bitcoin returns and volatility, when measured by applying several transformations on time series for the trading volume or the SVI Google searches index. Specifically, best candidate models are selected via the so-called Box–Jenkins methodology and by maximizing out-of-sample forecasting performance. Overall, we can conclude that trading volume-related measures affect both the mean and the volatility of the cryptocurrency returns, while Internet searches volume mainly affects the volatility. An interesting side finding is that the inclusion of attention measures in model specification makes forecast estimates more accurate.
The present paper investigates persistence and inter-dependence of bitcoin on other popular alternative coins. We employ fractional integration approach in our analysis of persistence, while a more recent fractional cointegration technique in VAR set-up, proposed by Johansen (2008), is used to investigate inter-dependence in pricing of the paired cryptocurrencies. Having partitioned the series into periods before and after the 2017/2018 cryptocurrency price crash, as determined by Bitcoin pricing, we obtain some interesting results. Higher persistence of price shocks is observed after the crash, which is probably due to speculative transactions among cryptocurrency traders, and more evidences of non-mean reversions are revealed, implying chances of further price fall in cryptocurrencies. Cointegration relationships between bitcoin and alternative coins exist in both periods, with weak correlations observed mostly in the post-crash period. Our results further indicate the possibility of market efficiency between pair of cryptocurrency. Generally, we hope the findings will serve as guide to investors in cryptocurrency markets.
We investigate the extent to which Bitcoin price fluctuations are associated with investors’ sentiment disagreement. We employ three textual sentiment analysis techniques: 1) a Python library offered by the Computational Linguistics and Psycholinguistics Research Center; 2) Loughran and McDonald’s (2011) dictionary; and 3) semantic orientation by the point-wise mutual information method. The results show that investors’ attention and sentiment disagreement induce extremely high volatility and jumps in Bitcoin prices. These findings complement existing studies on how investors’ sentiment manifests in asset prices.
This paper uses wavelet coherence and cross wavelet transform approaches to examine co-movement between Bitcoin and five major cryptocurrencies (Dash, Ethereum, Litecoin, Monero and Ripple) and their portfolio risk implications. The results show evidence of co-movements in time frequency space with leading relationships of Bitcoin with Dash, Monero and Ripple, lagging relationship with Ethereum, and out of phase movements with Litecoin. By considering different portfolios (risk-minimizing portfolio, equally weighted portfolio and hedging portfolio), we show evidence that a mixed portfolio (Bitcoin with other cryptocurrencies) provides better diversification benefits for investors and portfolio managers. Finally, an Ethereum-Bitcoin (Monero-Bitcoin) hedging portfolio offers the highest risk reductions and hedging effectiveness under medium and long term (short term) horizon. The results of downside risk reductions are time horizon dependent.
This study empirically investigates the statistical characteristics and predictability of Bitcoin return and volatility. The distribution of Bitcoin returns and volatility display a fat right tail and high central parts. Bitcoin does not show the dynamic property of volatility persistence, contrary to stylized facts in financial time series. Also, the autoregressive model using past volatility does not well work in predicting changes in Bitcoin volatility for future periods. Investor sentiment regarding Bitcoin has a significant information value for explaining changes in Bitcoin volatility for future periods. These results suggest that Bitcoin appears to be an investment asset with high volatility and dependence on investor sentiment rather than a monetary asset.
Early analysis of Bitcoin concluded that it did not meet the economic conditions to be classified as a currency. Since this conclusion, interest in Bitcoin has increased substantially. We investigate whether the introduction of futures trading in Bitcoin is able to resolve the issues that stopped Bitcoin from being considered a currency. Our analysis shows that spot volatility has increased following the appearance of futures contracts, that futures contracts are not an effective hedging instrument, and that price discovery is driven by uninformed investors in the spot market. We therefore argue that the conclusion that Bitcoin is a speculative asset rather than a currency is not altered by the introduction of futures trading.
This paper explores Bitcoin intraday technical trading based on artificial neural networks for the return prediction. In particular, our deep learning method successfully discovers trading signals through a seven layered neural network structure for given input data of technical indicators, which are calculated by the past time-series data over every 15 min. Under feasible settings of execution costs, the numerical experiments demonstrate that our approach significantly improves the performance of a buy-and-hold strategy. Especially, our model performs well for a challenging period from December 2017 to January 2018, during which Bitcoin suffers from substantial minus returns. Furthermore, various sensitivity analysis is implemented for the change of the number of layers, activation functions, input data and output classification to confirm the robustness of our approach.
We examine the existence and dates of pricing bubbles in Bitcoin and Ethereum, two popular cryptocurrencies using the (Phillips et al., 2011) methodology. In contrast to previous papers, we examine the fundamental drivers of the price. Having derived ratios that are economically and computationally sensible, we use these variables to detect and datestamp bubbles. Our conclusion is that there are periods of clear bubble behaviour, with Bitcoin now almost certainly in a bubble phase.
We correct the double spend race analysis given in Nakamoto's foundational Bitcoin article and find the exact closed-form formula for the probability of success of a double spend attack using the regularized incomplete beta function. We give the first proof of its exponential decay on the number of confirmations, often cited in the literature, and find an asymptotic formula. Larger number of confirmations are required compared to those given by Nakamoto. We also compute this probability conditional to the knowledge of the time of the confirmations. This provides a finer risk analysis than the classical one.
In this paper we draw upon the close relationship between statistical physics and mathematical finance to develop a suite of models for financial bubbles and crashes. The derived models allow for a probabilistic and statistical formulation of econophysics models closely linked to mainstream financial models. Applications include monitoring the stability of financial systems and the subsequent policy implications. We emphasise the timeliness of our contribution with an application to the two largest cryptocurrency markets: Bitcoin and Ripple. Results shed new light on emerging debates over the nature of cryptocurrency markets and competition between rival digital currencies.
A bona fide currency functions as a medium of exchange, a store of value, and a unit of account, but bitcoin largely fails to satisfy these criteria. Bitcoin has achieved only scant consumer transaction volume, with an average well below one daily transaction for the few merchants who accept it. Its volatility is greatly higher than the volatilities of widely used currencies, imposing large short-term risk upon users. Bitcoin's daily exchange rates exhibit virtually zero correlation with widely used currencies and with gold, making bitcoin useless for risk management and exceedingly difficult for its owners to hedge. Bitcoin prices of consumer goods require many decimal places with leading zeros, which is disconcerting to retail market participants. Bitcoin faces daily hacking and theft risks, lacks access to a banking system with deposit insurance, and is not used to denominate consumer credit or loan contracts. Bitcoin appears to behave more like a speculative investment than a currency.
Bitcoin is a peer-to-peer electronic payment system that operates as an independent currency. This paper is a philosophical investigation of the ontological constitution of Bitcoin. Using Slavoj Žižek’s ontological triad of the real, the symbolic and the imaginary, the paper distinguishes between three ideal typical theories of money: commodity theory, fiat theory, and credit theory. The constitution of Bitcoin is analysed by comparing the currency to each of these ideal types. It is argued that Bitcoin is commodity money without gold, fiat money without a state, and credit money without debt. In conclusion, it is suggested that Bitcoin poses an ideological challenge to conventional forms of money in so far as it not only provokes sedimented beliefs about money but also exposes the forms of exploitation, risk and even violence inherent in the existing system of state authorized credit money.
This paper explores the financial asset capabilities of bitcoin using GARCH models. The initial model showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. The asymmetric GARCH showed that bitcoin may be useful in risk management and ideal for risk averse investors in anticipation of negative shocks to the market. Overall bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar on a scale from pure medium of exchange advantages to pure store of value advantages.
Bitcoin is an online communication protocol that facilitates the use of a virtual currency, including electronic payments. Bitcoin's rules were designed by engineers with no apparent influence from lawyers or regulators. Bitcoin is built on a transaction log that is distributed across a network of participating computers. It includes mechanisms to reward honest participation, to bootstrap acceptance by early adopters, and to guard against concentrations of power. Bitcoin's design allows for irreversible transactions, a prescribed path of money creation over time, and a public transaction history. Anyone can create a Bitcoin account, without charge and without any centralized vetting procedure—or even a requirement to provide a real name. Collectively, these rules yield a system that is understood to be more flexible, more private, and less amenable to regulatory oversight than other forms of payment—though as we discuss, all these benefits face important limits. Bitcoin is of interest to economists as a virtual currency with potential to disrupt existing payment systems and perhaps even monetary systems. This article presents the platform's design principles and properties for a nontechnical audience; reviews its past, present, and future uses; and points out risks and regulatory issues as Bitcoin interacts with the conventional financial system and the real economy.