NicolĂČ Vallarano, Claudio J. Tessone, Tiziano Squartini
Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks , a generic name referring to a set of three different constructs: the Bitcoin Address Network , the Bitcoin User Network , and the Bitcoin Lightning Network . The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels.
The present work investigates the impact on financial intermediation of distributed ledger technology (DLT), which is usually associated with the blockchain technology and is at the base of the cryptocurrencies' development. "Bitcoin" is the expression of its main application since it was the first new currency that gained popularity some years after its release date and it is still the major cryptocurrency in the market. For this reason, the present analysis is focused on studying its price determination, which seems to be still almost unpredictable. We carry out an empirical analysis based on a cost of production model, trying to detect whether the Bitcoin price could be justified by and connected to the profits and costs associated with the mining effort. We construct a sample model, composed of the hardware devices employed in the mining process. After collecting the technical information required and computing a cost and a profit function for each period, an implied price for the Bitcoin value is derived. The interconnection between this price and the historical one is analyzed, adopting a Vector Autoregression (VAR) model. Our main results put on evidence that there aren't ultimate drivers for Bitcoin price; probably many factors should be expressed and studied at the same time, taking into account their variability and different relevance over time. It seems that the historical price fluctuated around the model (or implied) price until 2017, when the Bitcoin price significantly increased. During the last months of 2018, the prices seem to converge again, following a common path. In detail, we focus on the time window in which Bitcoin experienced its higher price volatility; the results suggest that it is disconnected from the one predicted by the model. These findings may depend on the particular features of the new cryptocurrencies, which have not been completely understood yet. In our opinion, there is not enough knowledge on cryptocurrencies to assert that Bitcoin price is (or is not) based on the profit and cost derived by the mining process, but these intrinsic characteristics must be considered, including other possible Bitcoin price drivers.
Beata Szetela, Grzegorz Mentel, Urszula Mentel, Yuriy Bilan
The crypto exchanges operate primarily on the internet, where the speed of information spreading is significant. Therefore, it is expected that there should be no significant differences among the individual exchanges concerning the same asset being traded. Prices should quickly reach comparable values on all stock exchanges, and they should return to equilibrium in a relative time frame. Hence, the investors, while making decisions on the selection of a cryptocurrency market, should be guided primarily by the exchange security considerations, its flexibility, availability of a product offer, and costs of order processing. The work aims to check whether virtual currency exchanges differ from each other in the context of directional movement, both in an upward and downward trend. To achieve the objective of the paper, we used Directional Movement Index, supported by the Directional Indicators, to compare the distribution of the strength of the directional movement across three different cryptocurrency exchanges (Bitstamp, Coinbase, Kraken) within the up and the downward price movement phase. The comparison is made based on the results of the non-parametrical tests such as Wilcoxon test, Hodges Lehmann test, Ansari-Bradley test, and Conover test. The results show that theoretically, the choice of a cryptocurrency exchange in an upward trend will cause no significant difference for an investor and its strategy. However, the choice of a stock exchange in a downward trend may have a substantial impact on the rates of return.
Bitcoin prices have fluctuated greatly, and news media have warned investors about a possible price bubble, arguing that the fluctuation arises mainly from people's blind pursuit of short-term trends. Despite these increasing concerns, however, only a few studies have addressed them. This article examines the problem using agent-based modeling. In our model, agents are designed to interact with one another in two ways: Price and social interactions. In their price interactions, agents adopt one of three strategies (fundamentalist, momentum trading, and contrarian trading) in investing at each time and adopt a strategy to maximize their expected benefits. In their social interactions, uninvolved agents become involved at various times based on their network properties (word-of-mouth effect). To examine the distinctive properties of Bitcoin from a comparative perspective, two representative currencies (Euro and Turkish lira) and two financial assets (Nasdaq and Nasdaq leverage index) are used in the agent-based model. The results show that the fraction of fundamentalists and price volatility have mutual Granger causal relationships overall. Also, no significant differences are found in the parameters of social interaction. These results are contrary to commonly held beliefs that Bitcoin prices are merely a result of blind pursuit and herding behavior.
The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.
Economic theory suggests that introduction of derivative contracts can improve the informational efficiency of the underlying asset prices (Danthine, 1978). In this study, we examine the impact of the introduction of Bitcoin futures on price clustering in Bitcoin. Our findings suggest that price clustering in Bitcoin meaningfully decreases post the introduction of its futures contracts.
Optimizations given historical data unsurprisingly produce sizeable allocations to Bitcoin (XBT). But further analyses of risks raise questions, even abstracting from expected returns. GARCH-based measures of dynamic XBT volatility and covariance suggest that optimal weights change over time. Also, quantile regressions indicate that conditional XBT returns with respect to the S&P 500 are modestly positively skewed. Yet benevolent symmetry is hardly stable or consistent along the distribution. Spectral analysis shows that the XBT volatility primarily owes to higher-frequency cycles. Nonetheless, XBT betas are substantially greater, and notably positive, over longer cycles compared with shorter cycles, which implies that XBT has been a much less effective strategic hedge. Dynamic principal components analysis indicates that individual coinsâ exposures to the âcrypto market factorâ have likely increased meaningfully enough over time to diminish diversification benefits. <b>TOPICS:</b>Currency, portfolio construction, portfolio theory <b>Key Findings</b> âą Standard mean-variance portfolio optimizations given historical data unsurprisingly produce sizeable allocations to Bitcoin (XBT). But further analyses of risks raise questions, especially for passive investors and abstracting from expected returns. For example, GARCH-based measures of dynamic XBT volatility and covariance suggest that optimal portfolio weights change substantially over time. âą Quantile regressions indicate that conditional XBT returns with respect to the S&P 500 are modestly positively skewed, arguably unlike even safe-haven assets such as US Treasuries. However, this comparatively benevolent symmetry is hardly stable or consistent along the distribution. âą Spectral analysis shows that the XBT volatility primarily owes to higher-frequency cycles, much like common asset classes. Nonetheless, XBT betas are substantially greater, and notably positive, over longer cycles compared with shorter cycles, which implies that XBT has been a much less effective strategic hedge. Also, dynamic principal components analysis indicates that individual coinsâ exposures to the âcrypto market factorâ have likely increased meaningfully enough over time to diminish diversification benefits for passive investors.
The mining of bitcoin is modeled using a system dynamics model that represents both the mechanism of coin creation and the adjustment of the network hash rate based on the economic incentive of mining. The results show that the past evolution of the network hash rate can be explained, to a large extent, by an efficient market hypothesis applied to the mining of blocks. The possibility of a decreasing trend in the network hash rate from the halving event of May 2020 is exposed, implying that the network may be close to âpeak hashâ if the price of bitcoin and the revenues from transaction fees will be insufficient to cover the operational expenditures of mining.
Bitcoinâs evolution has attracted the attention of investors and researchers looking for a better understanding of the efficiency of cryptocurrency markets, considering their prices and volatility. The purpose of this paper is to contribute to this understanding by studying the degree of persistence of the Bitcoin measured by the Hurst exponent, considering prices from the Brazilian market, and comparing with Bitcoin in USD as a benchmark. We applied Detrended Fluctuation Analysis (DFA), for the period from 9 April 2017 to 30 June 2018, using daily closing prices, with a total of 429 observations. We focused on two prices of Bitcoins resulting from negotiations made by two different Brazilian financial institutions: Foxbit and Mercado. The results indicate that Mercado and Foxbit returns tend to follow Bitcoin dynamics and all of them show persistent behavior, although the persistence in slightly higher for the Brazilian Bitcoin. However, this evidence does not necessarily mean opportunities for abnormal profits, as aspects such as liquidity or transaction costs could be impediments to this occurrence.
Charles Bertucci, Louis Bertucci, JeanâMichel Lasry, PierreâLouis Lions
We present an analysis of the Proof-of-Work consensus algorithm, used on the Bitcoin blockchain, using a Mean Field Game framework. Using a master equation, we provide an equilibrium characterization of the total computational power devoted to mining the blockchain (hashrate). From a simple setting we show how the master equation approach allows us to enrich the model by relaxing most of the simplifying assumptions. The essential structure of the game is preserved across all the enrichments. In deterministic settings, the hashrate ultimately reaches a steady state in which it increases at the rate of technological progress. In stochastic settings, there exists a target for the hashrate for every possible random state. As a consequence, we show that in equilibrium the security of the underlying blockchain is either $i)$ constant, or $ii)$ increases with the demand for the underlying cryptocurrency.
Purpose To show that when volume of trades is taken into consideration, Bitcoin does not seem as volatile as it claimed. Further, to study the relationship between Bitcoin trading volume, volatility and returns, and the asymmetry in response to economic information for the period from July 2010 to November 2017. Design/methodology/approach Comparison of Bitcoin price volatility with that of six currencies and gold. We repeat the analysis using returns divided by volume. We examine the relationship between volume, returns and volatility, and the asymmetry of the reaction of the volatility to economic news using asymmetric models (EGARCH) run for four meaningful distinct time periods/subsamples. Findings Positive and significant relationship between (1) volume and volatility after 2013 (year Bitcoin became popular) and (2) volume and returns before the Mt. Gox hack. During the euphoric period, starting at the beginning of 2013 until the Mt. Gox hack, unexpected increases in Bitcoin returns increased Bitcoin volatility more than unexpected, equally sized decreases (asymmetry). Originality/value We take into consideration the volume of trades to show that Bitcoin volatility seems high because of the low volume of trades. We study an extended time period, not covered by other studies. We divide our sample into four meaningful time periods based on important events in Bitcoin market history. This is important for a new market such as the Bitcoin market; the relationships under study are very important in markets where participants rely on technical analysis in the absence of reliable fundamental methodology to measure the intrinsic value of the asset.
Shahar Somin, Goren Gordon, Alex Pentland, Erez Shmueli · 5 authors
Following the birth of Bitcoin and the introduction of the Ethereum ERC20 protocol a decade ago, recent years have witnessed a growing number of cryptographic tokens that are being introduced by researchers, private sector companies and NGOs. The ubiquitous of such Blockchain based cryptocurrencies give birth to a new kind of rising economy, which presents great difficulties to modeling its dynamics using conventional semantic properties. Our work presents the analysis of the dynamical properties of the ERC20 protocol compliant crypto-coins' trading data using a network theory prism. We examine the dynamics of ERC20 based networks over time by analyzing a meta-parameter of the network, the power of its degree distribution. Our analysis demonstrates that this parameter can be modeled as an under-damped harmonic oscillator over time, enabling a year forward of network parameters predictions.
Lorenzo Lucchini, Laura Alessandretti, Bruno Lepri, Angela Gallo · 5 authors
"Code is law" is the funding principle of cryptocurrencies. The security, transferability, availability and other properties of a crypto-asset are determined by the code through which it is created. If code is open source, as it happens for most cryptocurrencies, this principle would prevent manipulations and grant transparency to users and traders. However, this approach considers cryptocurrencies as isolated entities thus neglecting possible connections between them. Here, we show that 4% of developers contribute to the code of more than one cryptocurrency and that the market reflects these cross-asset dependencies. In particular, we reveal that the first coding event linking two cryptocurrencies through a common developer leads to the synchronisation of their returns in the following months. Our results identify a clear link between the collaborative development of cryptocurrencies and their market behaviour. More broadly, our work reveals a so-far overlooked systemic dimension for the transparency of code-based ecosystems and we anticipate it will be of interest to researchers, investors and regulators.
There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.
Abstract The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency. Using the error correction model, the relationship between the price of the virtual currency, Bitcoin, and the number of Google searches that used the terms bitcoin , bitcoin crash and crisis between December 2012 and February 2018 is analyzed. The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period. The Johansen (J Econ Dyn Control 12:231-254, 1988) test demonstrates that the price and number of searches on Google for the first two terms are cointegrated. This research indicates that there are strong short-term and long-term dynamics among attractiveness factors, suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase. In contrast, an increase in market mistrust over a collapse of the currency, as measured by the term bitcoin crash , is followed by a fall in price. Intense world economic crisis events appear to have a strong impact on interest in the virtual currency. This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment, increasing its price. Based on it, bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.
Cryptocurrencies have recently captured the interest of the econometric literature, with several works trying to address the existence of bubbles in the price dynamics of Bitcoins and other cryptoassets. Extremely rapid price accelerations, often referred to as explosive behaviors, followed by drastic drops pose high risks to investors. From a risk management perspective, testing the explosiveness of individual cryptocurrency time series is not the only crucial issue. Investigating co-explosivity in the cryptoassets, i.e., whether explosivity in one cryptocurrency leads to explosivity in other cryptocurrencies, allows indeed to take into account possible shock propagation channels and improve the prediction of market collapses. To this aim, our paper investigates the relationships between the explosive behaviors of cryptocurrencies through a unit root testing approach.
Chad Albrecht, Steven R. Hawkins, Kristopher McKay Duffin
Cryptocurrency, and especially Bitcoin, has struggled to gain recognition as a legitimate currency from governments, financial institutions, and consumers. This has occurred because many analysts and consumers believe that Bitcoin is not a stable and consistent store of value, a unit of measurement, or a medium of exchange. One way to overcome this challenge is for Bitcoin to be used as both a currency and store of value by a greater percentage of the worldâs population. This paper seeks to identify how a change in Bitcoinâs monetary measurement (or denomination) can more easily facilitate Bitcoin transactions to increase its use. Specifically, we posit that applying whole number bias theory, from the cognitive psychology and mathematics fields, to Bitcoinâs unit of measurement will allow the value of Bitcoin to be referenced in smaller and easier tounderstand units with fewer numbers after the decimal pointâsuch as the âBitâ or the âSatoshi.â In the process, the use of Bitcoin will include more whole numbers and allow the general public to more easily assign value to Bitcoin in day-to-day transactions.
Abstract The primary purpose of this paper is to investigate whether a novel Markov regimeâswitching mixedâdata sampling (MRSâMIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jumpâdriven timeâvarying transition probability between the two regimes. Our results suggest that the proposed novel MRSâMIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the highâvolatility regime and switch between highâ and lowâvolatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2âweek and 1âmonth horizon forecasts.