Abstract This chapter focuses on the characterisation and treatment of cryptocurrencies in the common law of property, using Bitcoin as the main example. It first examines how a crypto-coin might be viewed as an object of property, and more specifically whether it could make a suitable object for any regime of property rights at all, before discussing the fungibility, specificity, scarcity and exclusivity of cryptocurrencies in the context of property law. It then considers whether crypto-coins fall into either of the two conventionally recognised categories of personal property: choses in possession or choses in action. It also explores the applicability of rules of derivative transfer of title to crypto-coin transactions, and how the standard rules of following and tracing may be used for the identification and tracing of cryptocurrencies through mixtures. Finally, it looks at remedies available at common law and in equity for enforcing titles to cryptocurrencies.
With the explosive growth in cryptocurrencies over the last couple of years, the cost of mining these technologies (the process through which users devote CPU power to operate the underlying blockchains) have similarly exploded. This paper examines one overarching question regarding this issue â what factor or factors explain the geographic distribution of cryptocurrency nodes (mining operations) across the world? In exploring this question, this research considers electricity price, internet access, Tor network relays, and others. Using node distribution data for Bitcoin and Ethereum â the two largest cryptocurrencies â this paper analyzes cross-sectional and panel data regression models, and establishes that electricity price has not played a significant role in this distribution up to this point, and concludes that the historical association between Tor relays and Bitcoin use has had a much greater impact. Lastly, this paper discusses the broader implications of its findings, and the potential areas of research for further understanding of this field.
Cathy YiâHsuan Chen, Wolfgang Karl Härdle, Ai Jun Hou, Ning Wang
The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, hu.berlin/crix. The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market for contingent claims hat not been built up yet. A reason is certainly the lack of pricing tools that are based on solid financial econometric tools. Here a first step towards pricing of derivatives of this new asset class is presented. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We calibrate SVCJ by MCMC and obtain interpretable jump processes and then via simulation price options. The jumps present in the cryptocurrency fluctutations are an essential component. Concrete examples are given to establish an OCRIX exchange platform trading options on CRIX.
We investigate the crossâcorrelations of returnâvolume relationship of the Bitcoin market. In particular, we select eight exchange rates whose trading volume accounts for more than 98% market shares to synthesize Bitcoin indexes. The empirical results based on multifractal detrended crossâcorrelation analysis (MFâDCCA) reveal that (1) the nonlinear dependencies and powerâlaw crossâcorrelations in returnâvolume relationship are found; (2) all crossâcorrelations are multifractal, and there are antipersistent behaviors of crossâcorrelation for q = 2; (3) the price of small fluctuations is more persistent than that of the volume, while the volume of larger fluctuations is more antipersistent; and (4) the rolling window method shows that the crossâcorrelations of returnâvolume are antipersistent in the entire sample period.
Frode KjĂŚrland, Maria Meland, Are Oust, Vilde Ăyen
The purpose of this study is to uncover factors that explain Bitcoin's price fluctuations. The price of the cryptocurrency Bitcoin is volatile and has increased from zero in 2009 to more than 19500 USD in December 2017. To explain the price movements we have estimated two autoregressive distributed lag models by using ordinary least squares regression. The data includes 279 weekly observations from 18.09.2011 to 05.02.2017 (before the extreme development from the summer of 2017). The dependent variable is the Bitcoin price and the analysis has examined nine independent variables. Our main finding and contribution is that political incidents and statements (âshocksâ) are significant drivers of Bitcoin's price. Moreover, the volume of Bitcoin and Bitcoin's price has a significant, negative relationship. The interest of Bitcoin, measured by Google searches, has a positive, significant relationship with Bitcoin's price. The study does not find evidence for Bitcoin being a safe haven investment.
Zhenghui Li, Hao Dong, Zhehao Huang, Pierre Failler
The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of VFAs. The results show that the influences of speculation and investor attention on the risks of VFAs are significantly positive at all regimes, while market interoperability only admits a positive impact on risk under high risk regime. All of the three factors exert asymmetric effects on risks in different regimes. Further study presents that the risk regime-switching also shows asymmetric characteristic but the medium risk regime is more stable than any others. Therefore, transactions of investors and arbitrageurs are monitored by certain policies, such as limiting the number of transactions or restricting the trading amount at high risk regime. However, when return risk is low, it will return to a medium level if we encourage investors to access.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013â2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).
Blockchain technology is having a deep impact on the financial and technical sectors providing a mechanism for the creation of decentralized currencies and a number of applications in different fields.At the core of the technology there is a consensus protocol enabling the maintenance of a distributed ledger.In general current systems are complex schemes that implement a combination of cryptographic algorithm, distributed techniques, and incentive driven behaviour.In this paper we focus on three of the most diffused platforms, i.e.Bitcoin, Ripple, and Ethereum, and provide a comparative analysis of their most important features such as the architecture, the scripting language, the economic and security properties.
Blockchain is a technology to exchange digital assets including cryptocurrencies, data, software, patents etc. Blockchain is designed in various types depending on different architectures and configurations for different purposes. The mathematical and cryptographic mechanisms and consensus algorithms, implemented in the Blockchain, guarantee trust, security, integrity and availability of transactions between participants. \nThis thesis is aimed to consider mathematical foundation and algorithmic aspects of Ethereum from the base network to high level of its functionality with the comparison to mathematical mechanisms of Bitcoin. In this thesis we describe Ethereum as stack of layers. Our mission is to extract cryptographic and mathematical mechanisms implemented in each layer. \nWe present how hash function is utilized to construct cost function and Hashcash protocol. Hashcash protocol is implemented in Proof of Work (PoW) mechanism. Also, we explain how hash function is used to connect blocks in the chain of blocks and how hash function is used to organize transactions in a Merkle tree and in a Merkle-Patricia-trie. \nThis thesis describes how Elliptic Curve Digital Signature Algorithm (ECDSA) and Lamport signing algorithm provide integrity of transactions. Elliptic Curve Integrated Encryption Scheme (ECIES) is hybrid encryption scheme which is utilized to provide confidentiality of transactions. The Elliptic Curve Cryptography (ECC) is used to disguise identities in both Bitcoin and Ethereum network. The new approaches to provide privacy are Ring signatures and Zk-Snarks. \nThis thesis also explains Bloom filter structure in Ethereum. The Bloom filter is a probabilistic data structure to test membership of elements in big data bases.
Decentralised issued crypto "currencies", like bitcoin, have the potential to drastically change the existing retail payment system and even the monetary system. Insights into the factors that influence their adoption are therefore crucial. Using a large representative sample of retailers that sell their products online, we find that acceptance of crypto payments is currently modest (2%), but there is substantial interest among retailers to adopt crypto payments in the near future. Consumer demand, net transactional benefits and perceived adoption effort influence adoption intention and actual acceptance by retailers. Regarding non-financial factors, our findings suggest that service providers who act as intermediaries between retailers, their customers, and providers of payment instruments play a crucial role as facilitators of competition and innovation in the online retail payments market by lowering such barriers. The most serious barrier for crypto acceptance seems to be a lack of consumer demand. Information from consumers indicate that those who possess cryptos, don't use it for online payments. It seems therefore unlikely that the adoption of cryptos by retailers will increase substantially, making it highly unlikely that cryptos like bitcoin will drastically change the existing retail payment system.
Do Bitcoin and other cryptocurrencies play a useful social role, or do they represent a social waste? Bitcoin is a decentralized recordkeeping system, with updating of the record of transactions in the blockchain.
A landmark security property of smart contracts is liquidity: in a non-liquid contract, it may happen that some funds remain frozen. The relevance of this issue is witnessed by a recent liquidity attack to the Ethereum Parity Wallet, which has frozen $${\sim }160M$$ USD within the contract, making this sum unredeemable by any user. We address the problem of verifying liquidity of Bitcoin contracts. Focussing on BitML, a contracts DSL with a computationally sound compiler to Bitcoin, we study various notions of liquidity. Our main result is that liquidity of BitML contracts is decidable, in all the proposed variants. To prove this, we first transform the infinite-state semantics of BitML into a finite-state one, which focusses on the behaviour of any given set of contracts, abstracting the context moves. With respect to the chosen contracts, this abstraction is sound and complete. Our decision procedure for liquidity is then based on model-checking the finite space of states of the abstraction.
Krishnendu Chatterjee, Amir Kafshdar Goharshady, Yaron Velner
Smart contracts are computer programs that are executed by a network of mutually distrusting agents, without the need of an external trusted authority. Smart contracts handle and transfer assets of considerable value (in the form of crypto-currency like Bitcoin). Hence, it is crucial that their implementation is bug-free. We identify the utility (or expected payoff) of interacting with such smart contracts as the basic and canonical quantitative property for such contracts. We present a framework for such quantitative analysis of smart contracts. Such a formal framework poses new and novel research challenges in programming languages, as it requires modeling of game-theoretic aspects to analyze incentives for deviation from honest behavior and modeling utilities which are not specified as standard temporal properties such as safety and termination. While game-theoretic incentives have been analyzed in the security community, their analysis has been restricted to the very special case of stateless games. However, to analyze smart contracts, stateful analysis is required as it must account for the different program states of the protocol. Our main contributions are as follows: we present (i)~a simplified programming language for smart contracts; (ii)~an automatic translation of the programs to state-based games; (iii)~an abstraction-refinement approach to solve such games; and (iv)~experimental results on real-world-inspired smart contracts.
Marian Gidea, Daniel Goldsmith, Yuri Katz, Pablo Roldan ¡ 5 authors
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple) before the digital market crash at the end of 2017 - beginning 2018. We introduce a methodology that combines topological data analysis with a machine learning technique -- $k$-means clustering -- in order to automatically recognize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor, and then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets, even though the relevant time series exhibit a highly erratic behavior.
Distributed ledger technology has gained wide popularity and adoption since the emergence of bitcoin in 2008 which is based on proof of work (PoW). It is a distributed, transparent and immutable database of records of all the transactions or events that have been shared and executed among the participants. All the transactions are verified and maintained by multiple nodes across a network without a central authority through a distributed cryptographic mechanism, a consensus protocol. It forms the core of this technology that not only validates the information appended to the ledger but also ensures the order in which it is appended across all the nodes. It is the foundation of its security, accountability and trust. While many researchers are working on improving the current protocol to be quantum resistant, fault-tolerant, and energy-efficient. Others are focused on developing different variants of the protocol, best suited for specific use cases. In this paper, we shall review different consensus protocols of distributed ledger technologies and their implementations. We shall also review their properties, concept and similar-work followed by a brief analysis.
Recent attention to Bitcoin and other cryptocurrencies has opened investors and the public to the realm of digital currency. Greater exposure around the world has led to a frenzy of entry into the market and a test into the long-term feasibility of Bitcoin being able to remain a functioning peer-to-peer (P2P), decentralized currency. Its main structure is supported by the Proof-of-Work (PoW) protocol in which users can elect to participate in determining transaction approval and ensuring an honest blockchain. This system relies on elected users to expend computational power and energy to solve puzzles to prove the accuracy of the networkâs transactions and create new blocks.\nEach cryptocurrency uses their own method to ensure blockchain accuracy, and this paper will focus on how a Proof-of-Stake (PoS) protocol is a superior algorithm to PoW by assigning mining ability equal to oneâs stake within a coin, rather than her energy consumption, among other factors. We will discuss Bitcoinâs PoW as a baseline for our eventual analysis of PoS in terms of advantages and performance metrics. The main factors that can be compared between the two protocols is how each system can prevent itself against a variety of attacks from adversarial users within the network, as well as long-term sustainability.\nFinally, we will use the Cardano (ADA) cryptocurrency by IOHK as a case study for understanding how their Ouroboros Praos PoS protocol works. Our goal is to show how long-term adoption of PoS framework is more realistic from an energy perspective than PoW.
We study key leakage in the context of cryptocurrencies. First, we consider the problem of explicit key leakage occurring on open-source intelligence platforms. To do this, we monitor the Pastebin feed from Sep 2017âMar 2018 to find exposed secret Bitcoin keys, revealing that attackers could have stolen 22.40 BTC worth roughly $178,000 given current exchange rates. Then, we focus on implicit key leakage by exploiting the wrong usage of cryptographic primitives and scan Bitcoinâs blockchain for ECDSA nonce reuse. We systematically outline how an attacker can use duplicate r values to leak nonces and secret keys, which goes beyond the simple case where the same nonce and the same key have been used in conjunction more than once. Our results show that ECDSA nonce reuse has been a recurring problem in the Bitcoin ecosystem and has already been exploited by attackers. In fact, an attacker could have exploited nonce reuse to steal 412.80 BTC worth roughly $3.3 million.
On August 1, 2017, the Bitcoin blockchain experienced a hard fork.The hard fork, spurred by concerns over Bitcoin's scalability, resulted in an entirely new blockchain and an accompanying new cryptocurrency: Bitcoin Cash.However, the new blockchain relies on the history of transactions recorded on the old blockchain.Consequently, at the time of the hard fork, every holder of Bitcoin could have received an equal amount of Bitcoin Cash.This sudden receipt of Bitcoin Cash poses a variety of tax problems.Should the acquired cryptocurrency qualify as income?If so, how should taxpayers calculate this income?Current income taxation law suggests the Bitcoin/Bitcoin Cash hard fork produced gain that, for the most part, was immediately realized.Thus, most taxpayers that received Bitcoin Cash at the time of the hard fork should have reported its value as income to the Internal Revenue Service.However, due to a variety of practical concerns, including a lack of sufficient analogous situations, cryptocurrency's volatility, and the IRS's refusal to follow relevant regulations related to the taxation of "treasure trove," perhaps it would be best to reconsider this conclusion and explore a solution that permits taxation of Bitcoin Cash upon a subsequent sale.