Alessandra Cretarola, Gianna Figg-Talamanca, Marco Patacca
In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.
Christoph Kinkeldey, Jean‐Daniel Fekete, Petra Isenberg
BitConduite is a system we are developing for the visual exploration of financial activity on the Bitcoin network. Bitcoin is the largest digital pseudo-currency worldwide and its study is of increasing interest and importance to economists, bankers, policymakers, and law enforcement authorities. All financial transactions in Bitcoin are available in an openly accessible online ledger-the (Bitcoin) blockchain. Yet, the open data does not lend itself easily to an analysis of how different individuals and institutions-or entities on the network-actually use Bitcoin. Our system BitConduite offers a data transformation back end that gives us an entity-based access to the blockchain data and a visualization front end that supports a novel high-level view on transactions over time. In particular, it facilitates the exploration of activity through filtering and clustering interactions. We are developing our system with experts in economics and will conduct a formal user study to assess our approach of Bitcoin activity analysis.
Jonathan Chiu, Thorsten V. Koeppl, Chiu, Jonathan, Koeppl, Thorsten
How well can a cryptocurrency serve as a means of payment? We study the optimal design of cryptocurrencies and assess quantitatively how well such currencies can support bilateral trade. The challenge for cryptocurrencies is to overcome double-spending by relying on competition to update the blockchain (costly mining) and by delaying settlement. We estimate that the current Bitcoin scheme generates a large welfare loss of 1.4% of consumption. This welfare loss can be lowered substantially to 0.08% by adopting an optimal design that reduces mining and relies exclusively on money growth rather than transaction fees to finance mining rewards. We also point out that cryptocurrencies can potentially challenge retail payment systems provided scaling limitations can be addressed.
Bitcoin is a widely-spread payment instrument, but it is doubtful whether the proof-of-work (PoW) nature of the system is financially sustainable on the long term. To assess sustainability, we focus on the bitcoin miners as they play an important role in the proof-of-work consensus mechanism of bitcoin to create trust in the currency. Miners offer their services against a reward while recurring expenses. Our results show that bitcoin mining has become less profitable over time to the extent that profits seem to converge to zero. This is what economic theory predicts for a competitive market that has a single homogenous good. We analyze the actors involved in the bitcoin system as well as the value flows between these actors using the e3value methodology. The value flows are quantified using publicly available data about the bitcoin network. However, two important value flows for the miners, namely hardware investments and expenses for electricity power, are not available from public sources. Therefore, we contribute an approach to estimate the installed base of bitcoin hardware equipment over time. Using this estimate, we can calculate the expenses miner should have. At the end of our analysis period, the marginal profit of mining a bitcoin becomes negative, i.e., to a loss for the miners. This loss is caused by the consensus mechanism of the bitcoin protocol, which requires a substantial investment in hardware and significant recurring daily expenses for energy. Therefore, a sustainable crypto currency needs higher payments for miners or more energy efficient algorithms to achieve consensus in a network about the truth of the distributed ledger.
Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 events-which drove the value of Bitcoin in times of economic and geopolitical chaos. We use a Bayesian quantile regression to inspect how the structure of dependence of Bitcoin price and its determinants varies across the entire conditional distribution of Bitcoin price movements. In doing so, three groups of determinants were derived. The use of Bitcoin in trade and the uncertainty surrounding China's deepening slowdown, Brexit and India's demonetization were found to be the most potential contributors of Bitcoin price when the market is improving. The intense anxiety over Donald Trump being the president of United States was shown to be a positive determinant pushing up the price of Bitcoin when the market is functioning around the normal mode. The velocity of bitcoins in circulation, the gold price, the Venezuelan currency demonetization and the hash rate were found to be the fundamentals influencing the Bitcoin price when the market is heading into decline.
Alessandra Cretarola, Gianna Figà‐Talamanca, Marco Patacca
In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.
Digital currencies and cryptocurrencies have hesitantly started to penetrate the investors, and the next step will be the regulatory risk management framework. We examine the Value-at-Risk and Expected Shortfall properties for the major digital currencies, Bitcoin, Ethereum, Litecoin, and Ripple. The methodology used is GARCH modelling followed by Filtered Historical Simulation. We find that digital currencies are subject to a higher risk, therefore, to higher sufficient buffer and risk capital to cover potential losses.
Attempts to accurately measure the monetary velocity or related properties of Bitcoin have often attempted to either directly apply definitions from traditional macroeconomic theory or to use specialized metrics relative to the properties of the Blockchain such as bitcoin-days destroyed. In this paper, it is demonstrated that beyond being a useful metric, bitcoin-days destroyed has mathematical properties that allow one to calculate the average dormancy (time since last use in a transaction) of the bitcoins used in transactions over a given time period. In addition, transaction volume and average dormancy are shown to have unexpected significance in helping estimate the average size of the pool of traded bitcoins by virtue of the expression Little's Law, though only under limited conditions.
Abstract The philosophy of blockchain technology is concerned, among other things, with blockchain ontology, how it might be characterised, how it is being created, implemented, and adopted, how it operates in the world, and how it evolves over time. This paper concentrates on whether Bitcoin/blockchain can be considered a complex system and, if so, whether it is a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010‐Flash‐Crash‐type of chaotic regime, with catastrophic consequences for financial systems based on it. The paper starts by highlighting the relevant details of the Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end users (payers and payees), capital gains seekers, miners, full nodes maintainers, and developers, and their interactions. Then the Information Theory of Complex Systems is briefly discussed for later use. Finally, the blockchain is investigated with the help of Crutchfield's Statistical Complexity measure. The low non‐null statistical complexity value obtained suggests that the blockchain may be considered algorithmically complicated but hardly a complex system and unlikely to enter a chaotic regime.
Bitcoin is traded on exchanges which use an open limit order book. This paper investigates the microstructure of various bitcoin markets with respect to liquidity and private information processing. The markets are found to be fairly liquid, providing liquidity at a stable rate throughout the 24 hours trading period. The spread itself as well as the proportion attributed to adverse selection costs are high suggesting that private information is an important aspect in the bid-ask spread.
Monies are typically categorized as commodity or fiat, depending on whether the item in question is intrinsically worthless. In the case of bitcoin, it is not so clear. I consider the superficial subjective value argument often put forward by non-monetary economists and a more sophisticated payments technology argument. After dismissing both, I argue that there are two reasonable views on the value of bitcoin. One might claim bitcoin lacks intrinsic worth, in which case its value depends on foresight and coordination. Alternatively, one might claim that bitcoin has intrinsic worth, even if no one else accepts it, because some users have peculiar preferences. In either case, the existence of bitcoin calls into question the practical relevance of the regression theorem.
The blockchain constitutes a technology-based, rather than social or regulation based, means to lower uncertainty about one another in order to exchange value. However, its use may very well also lead to increased complexity resulting from having to subsume work that displaced intermediary institutions had performed. We present our perspective that smart contracts may be used to mitigate this increased complexity. We further posit that smart contracts can be delineated according to complexity: Smart contracts that can be verified objectively without much uncertainty belong in an inter- organizational context; those that cannot be objectively verified belong in an intra- organizational context. We state that smart contracts that implement a formal (e.g. mathematical or simulation) model are especially beneficial for both contexts: They can be used to express and enforce inter-organizational agreements, and their basis in a common formalism may ensure effective evaluation and comparison between different intra-organizational contracts. Finally, we present a case study of our perspective by describing Intellichain, which implements formal, agent-based simulation model as a smart contract to provide epidemiological decision support.
From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentors: Mina Lee and Li Yang
Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.
Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Saralees Nadarajah
We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.
Guglielmo Maria Caporale, Luis A. Gil‐Alana, Alex Plastun
This paper examines persistence in the cryptocurrency market. Two different long-memory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013–2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future values), and that its degree changes over time. Such predictability represents evidence of market inefficiency: trend trading strategies can be used to generate abnormal profits in the cryptocurrency market.