The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also - if economically convenient - to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels.
This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.
A blockchain is a distributed ledger comprised of practically unchange- able, digital recorded data in packages called blocks. Each block in the chain contains data and is cryptographically hashed. The blocks of hashed data draw upon the previous block in the chain, ensuring all data in the overall blockchain is untampered. Blockchain and Distributed Ledger ad- vantages are related to enhanced transparency in business applications and between the involved parties compared to using ordinary databases. Since the blockchain is cryptographically protected, it can be shared, al- lowing anyone to check the correctness of a transaction. Previously, this technology was mostly used for enabling public, decent- ralized digital currencies, known as cryptocurrencies, such as BitCoin and LiteCoin. In the latest years, however, additional use-cases have been de- signed, including non-money asset tokenization, digital identity and sup- ply chain management. Together with the rise of new use-cases, distrib- uted ledger technology frameworks emerged to assist and simplify the development process of such use-cases. These frameworks accelerate the development process at the cost of resource overhead. In this thesis, we use Hyperledger Fabric, a distributed ledger technology framework maintained by the Linux Foundation, to design, develop and analyze the performance of a use-case granted by DNV-GL. We explore the network resource cost of a transaction and model the network traffic flow. In addition, we measure and present the performance of this system and demonstrate why such a performance display alone is misleading.
The significant growth and adoption of Internet of Things (IoT) solutions has led to tremendous increase in the generation of data. The need for high speed data processing has become very important to meet with the ever increasing volume and velocity of IoT data, due to the large scale and distributed nature of IoT infrastructure and networks. Present cloud based technologies are struggling to meet up with these needs for real time data processing in the midst of enormous amounts of data. The success of bitcoin has inspired more research in the application of Distributed ledger technologies in various domains. The decentralized nature of these platforms have enabled security and privacy of data in previous research and their architecture has a potential for enabling large scale decentralized data processing. In this paper, we identify some open areas of research in the use of distributed ledger technology and propose a framework for storing, analyzing and ensuring the security of large volumes of IoT data.
Ayushi Sharma, Shashwat Tiwari, Nitin Arora, S. C. Sharma
Blockchain is an emerging technology that can radically improve transactions security at banking, supply chain, and other transaction networks. It's estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering. This book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack, and cryptocurrency including Facebook's Libra will be elucidated. Also, the book will address the relationship between Blockchain technology, Internet of Things (IoT), Artificial Intelligence (AI), Cybersecurity, Digital Transformation and Quantum Computing. Readers will understand the inner workings and applications of this disruptive technology and its potential impact on all aspects of the business world and society. A look at the future trends of Blockchain Technology will be presented in the book.
Canh Phuc Nguyen, Nguyen Quang Binh, Thanh Dinh Su
The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.
Guglielmo Maria Caporale, Woo-Young Kang, Fabio Spagnolo, Nicola Spagnolo
This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015–2/28/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets.
Vasily Derbentsev, Natalia Datsenko, Olga Stepanenko, Vitaly Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
Decentralized finance has evolved as a major contender for traditional banking systems over the last few years. Evolution in blockchain and cryptography technologies are the driving forces for decentralized finance’s growth. The emergence of Bitcoin in the finance system was a major driving force toward the tremendous growth of decentralized finance. However, with various platforms merging every day, the decentralized finance sector is still in its early, unorganized stages. The current decentralized finance market is chaotic. With a new “coin” being introduced almost every month, standardization is highly lacking in the system. DeFi already has several different applications available. For instance, one can purchase stable coins, or assets pegged to a national currency, on decentralized exchanges, move the assets to a lending platform that is also decentralized to earn interest, and then add the interest-earning instruments to a decentralized liquidity pool or an on-chain investment fund. DeFi enterprises frequently aim at decentralized decision-making, or governance, in everything from the user fees to the products they provide. A decentralized program may be started by one person or a small number of individuals, but as the project gathers traction, its leaders frequently try to step down and cede control to the user base. A decentralized autonomous organization that has its rules and regulations written into computer code and that may issue governance tokens, which allow its holders a voice in decisions rather than allowing the decision-making to a centralized government authority as in case of traditional finance, could represent this transition. While on one side, world governments are still trying to grasp and regulate the sector, on the other side, the technology’s reach has been very limited. Undoubtedly, the emergence of blockchain-based decentralized finance is massively influencing our current finance technology industry. In this chapter, we discuss the current growth in the FinTech industry and the blockchain-based decentralized finance sector. Furthermore, we discuss how decentralized finance can be used in the current FinTech industry.
Cryptocurrencies,1 like bitcoin, raise new legal questions due to their innovative technological concepts. While academic research covers nearly all areas of the technological concepts of those currencies, legal studies focus only on a few topics. The papers that have been published so far discuss mainly economic law, tax law, and financial regulations. At the same time, governments are starting to explicitly regulate cryptocurrencies in terms of anti-money-laundering (AML) and to clarify or strengthen the legal basis for prosecuting crimes in the context of cryptocurrencies. Furthermore, criminal investigation in the context of cryptocurrencies is intensifying with the rising number of cryptocurrency-related crimes. Moreover, governments should also start to consider crime prevention in the context of cryptocurrencies. AML regulation, crime prevention, and prosecution have to take heed of the fundamental rights of the citizens affected. To date, legal research has not discussed the relationship between AML regulation (regarding cryptocurrencies), crime prevention (in conjunction with cryptocurrencies), the prosecution of crimes involving cryptocurrencies and fundamental rights. Many future regulatory concepts will collide with the fundamental right to property of the owners of cryptocurrency units and the freedom to pursue a trade or profession of owners and operators of exchange platforms, mining pools, etc. In cryptocurrencies organized as peer-to-peer systems, the freedom of association also has to be mentioned. With particular regard to prosecution, law enforcement agencies restrict the freedom of telecommunication, data privacy (including the right to informational self-determination), freedom of expression, and the freedom of information. Whenever some of these fundamental rights are impinged upon, regulation concepts and investigation or prosecution approaches must be provided for by law and must fulfill the criterion of necessity. Further interdisciplinary research is needed to develop efficient and legit prevention as well as criminal investigation concepts.
Purpose - This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios. Design/methodology/approach - The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios. Findings - The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios. Originality/value - This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.
The Dark Web is notorious for being a major distribution channel of harmful content as well as unlawful goods.Perpetrators have also used cryptocurrencies to conduct illicit financial transactions while hiding their identities.The limited coverage and outdated data of the Dark Web in previous studies motivated us to conduct an in-depth investigative study to understand how perpetrators abuse cryptocurrencies in the Dark Web.We designed and implemented MFScope, a new framework which collects Dark Web data, extracts cryptocurrency information, and analyzes their usage characteristics on the Dark Web.Specifically, MFScope collected more than 27 million dark webpages and extracted around 10 million unique cryptocurrency addresses for Bitcoin, Ethereum, and Monero.It then classified their usages to identify trades of illicit goods and traced cryptocurrency money flows, to reveal black money operations on the Dark Web.In total, using MFScope we discovered that more than 80% of Bitcoin addresses on the Dark Web were used with malicious intent; their monetary volume was around 180 million USD, and they sent a large sum of their money to several popular cryptocurrency services (e.g., exchange services).Furthermore, we present two real-world unlawful services and demonstrate their Bitcoin transaction traces, which helps in understanding their marketing strategy as well as black money operations.
Through the application of Diagonal BEKK and Asymmetric Diagonal BEKK methodologies to intra-day data for eight cryptocurrencies, this paper investigates not only conditional volatility dynamics of major cryptocurrencies, but also their volatility co-movements. We first provide evidence that all conditional variances are significantly affected by both previous squared errors and past conditional volatility. It is also shown that both methodologies indicate that cryptocurrency investors pay the most attention to news relating to Neo and the least attention to news relating to Dash, while shocks in OmiseGo persist the least and shocks in Bitcoin persist the most, although all of the considered cryptocurrencies possess high levels of persistence of volatility over time. We also demonstrate that the conditional covariances are significantly affected by both cross-products of past error terms and past conditional covariances, suggesting strong interdependencies between cryptocurrencies. It is also demonstrated that the Asymmetric Diagonal BEKK model is a superior choice of methodology, with our results suggesting significant asymmetric effects of positive and negative shocks in the conditional volatility of the price returns of all of our investigated cryptocurrencies, while the conditional covariances capture asymmetric effects of good and bad news accordingly. Finally, it is shown that time-varying conditional correlations exist, with our selected cryptocurrencies being strongly positively correlated, further highlighting interdependencies within cryptocurrency markets.
Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.
Abstract Bitcoin is a distributed system. The dilemma it poses to the legal systems is that it is hardly possible to regulate a distributed network in a centralized fashion, as decentralized cryptocurrencies are antithetical to the existing centralized structure of monetary and financial regulation. This article proposes a more nuanced policy recommendation for regulatory intervention in the cryptocurrency ecosystem, which relies on a decentralized regulatory architecture built upon the existing regulatory infrastructure and makes use of the existing and emerging middlemen. It argues that instead of regulating the technology or the cryptocurrencies at the code or protocol layer, the regulation should target their use-cases. Such a regulatory strategy can be implemented through directing the edicts of regulation towards the middlemen and can be enforced by the existing financial market participants and traditional gatekeepers such as banks, payment service providers and exchanges, as well as large and centralized node operators and miners.
Kentaroh Toyoda, P. Takis Mathiopoulos, Tomoaki Ohtsuki
Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.
Open access
Blockchain Technology Applications and Security
Spam and Phishing Detection
Advanced Steganography and Watermarking Techniques
Abstract This paper studies the volatility of Bitcoin and determines the importance of jumps and structural breaks in forecasting volatility. We show the importance of the decomposition of realized variance in the in‐sample regressions using 18 competing heterogeneous autoregressive (HAR) models. In the out‐of‐sample setting, we find that the HARQ‐F‐J model is the superior model, indicating the importance of the temporal variation and squared jump components at different time horizons. We also show that HAR models with structural breaks outperform models without structural breaks across all forecasting horizons. Our results are robust to an alternative jump estimator and estimation method.