The recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, making investors and financial regulators aware of the importance of modeling them correctly. The Lvy's stable distribution is one of the attractive distributions that well describes the fat tails and scaling phenomena in economic systems. In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized by a non-Gaussian Lvy's stable distribution with ' 1:4 under certain conditions on time intervals ranging roughly from 30 min to 4 h. Our arguments are developed under quantitative valuation defined as a distance function using the Parseval's relation in addition to the theoretical background of the General Central Limit Theorem (GCLT). We also discuss the model-fitting for returns by employing the method based on likelihood ratios. Even though the cubic power-law model is a better fitting model than the Lvy's stable model in the tail part of returns, the Lvy's stable model outperforms the fit for the entire and wider range of returns. Our approach can be extended for further analysis of statistical properties and contribute to developing proper applications for financial modeling.
This letter questions the true nature (true versus spurious) of the Long Range Dependence (LRD) behavior observed in the returns and volatility series of four Cryptocurrencies (CC). Using a robust approach, this letter shows that the LRD behavior exhibited by the returns and volatility series of Bitcoin, Litecoin, and Ripple is a true behavior, and not a statistical artifact. As for Ethereum, the results show that the true LRD is only supported for the volatility series. Our results confirm the inefficiency of all the considered markets, with the exception of Ethereum.
Currently, Cryptocurrency is one of the trending areas of research among researchers. Many researchers may analyze the cryptocurrency features in several ways such as market price prediction, the impact of cryptocurrency in real life and so on. In this paper, we focus on market price prediction of the number of cryptocurrencies based on their historical trend. For our study, we tried to understand and identify the daily trends in the cryptocurrency market which analyzing the features related to the price of cryptocurrency. Our dataset consists of over nine features relating to the cryptocurrency price recorded daily over the period of 6 months. We applied some machine-learning algorithms to predict the daily price change of cryptocurrencies.
Aurelio F. Bariviera, Luciano Zunino, Osvaldo A. Rosso
This paper discusses the dynamics of intraday prices of twelve cryptocurrencies during last months' boom and bust. The importance of this study lies on the extended coverage of the cryptoworld, accounting for more than 90\% of the total daily turnover. By using the complexity-entropy causality plane, we could discriminate three different dynamics in the data set. Whereas most of the cryptocurrencies follow a similar pattern, there are two currencies (ETC and ETH) that exhibit a more persistent stochastic dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a random walk. Consequently, similar financial assets, using blockchain technology, are differentiated by market participants.
The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.
Correlation networks were used to detect characteristics which, although\nfixed over time, have an important influence on the evolution of prices over\ntime. Potentially important features were identified using the websites and\nwhitepapers of cryptocurrencies with the largest userbases. These were assessed\nusing two datasets to enhance robustness: one with fourteen cryptocurrencies\nbeginning from 9 November 2017, and a subset with nine cryptocurrencies\nstarting 9 September 2016, both ending 6 March 2018. Separately analysing the\nsubset of cryptocurrencies raised the number of data points from 115 to 537,\nand improved robustness to changes in relationships over time. Excluding USD\nTether, the results showed a positive association between different\ncryptocurrencies that was statistically significant. Robust, strong positive\nassociations were observed for six cryptocurrencies where one was a fork of the\nother; Bitcoin / Bitcoin Cash was an exception. There was evidence for the\nexistence of a group of cryptocurrencies particularly associated with Cardano,\nand a separate group correlated with Ethereum. The data was not consistent with\na token's functionality or creation mechanism being the dominant determinants\nof the evolution of prices over time but did suggest that factors other than\nspeculation contributed to the price.\n
In this note, I return to Coase (1937), on its 80th anniversary, to assess whether its logic and insight can be reconciled with the blockchain revolution. I argue that, indeed, it can, and propose the existence of a third method of organizing economic activity in a specialized exchange economy, in addition to the two that Coase considered. I call it the cryptographic stigmergy. If there be such merit in the argument here, let it be dedicated to the memory of Ronald Coase.
Blockchain technology, which has been known by mostly small technological circles up until recently, is bursting throughout the globe, with a potential economic and social impact that could fundamentally alter traditional financial and social structures. Issuing cryptocurrencies on top of the Blockchain system by startups and private sector companies is becoming a ubiquitous phenomenon, inducing the trading of these crypto-coins among their holders using dedicated exchanges. Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. Analyzing and modeling the dynamics of the "social signals" of this network can contribute to our understanding of this ecosystem and the forces acting within in. This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins' trading data. Considering all trading wallets as a network's nodes, and constructing its edges using buy--sell trades, we can analyze the network properties of the ERC20 network. Examining several periods of time, and several data aggregation variants, we demonstrate that the network displays strong power-law properties. These results coincide with current network theory expectations, however nonetheless, are the first scientific validation of it, for the ERC20 trading data. The data we examined is composed of over 30 million ERC20 tokens trades, performed by over 6.8 million unique wallets, lapsing over a two years period between February 2016 and February 2018.
Optimization methods are used to determine equilibria of investment in cryptocurrencies. The basic assumptions involve existence of a core group (the "wealthy") that fears the loss of substantial assets through government seizure. Speculators constitute another group that tends to introduce volatility and risk for the wealthy. The wealthy must divide their assets between the home currency and the cryptocurrency, while the government decides on the probability of seizing a fraction the assets of this group. Under the assumption that each group exhibits risk aversion through a utility function, we establish the existence and uniqueness of Nash equilibrium. Also examined is the more realistic optimization problem in which the government policy cannot be reversed, while the wealthy can adjust their allocation in reaction to the government's designation of probability. The methodology leads to an understanding the equilibrium market capitalization of cryptocurrencies.
This study proposes a strategy to make the lookback option cheaper and more practical, and suggests the use of its properties to reduce risk exposure in cryptocurrency markets through blockchain enforced smart contracts and correct for informational inefficiencies surrounding prices and volatility. This paper generalizes partial, discretely-monitored lookback options that dilute premiums by selecting a subset of specified periods to determine payoff, which we call amnesiac lookback options. Prior literature on discretely-monitored lookback options considers the number of periods and assumes equidistant lookback periods in pricing partial lookback options. This study by contrast considers random sampling of lookback periods and compares resulting payoff of the call, put and spread options under floating and fixed strikes. Amnesiac lookbacks were priced with Monte Carlo simulations of Gaussian random walks under equidistant and random periods. Results were compared to analytic and binomial pricing models for the same derivatives. Simulations show diminishing marginal increases to the fair price as the number of selected periods is increased. The returns correspond to a Hill curve whose parameters are set by interest rate and volatility. We demonstrate over-pricing under equidistant monitoring assumptions with error increasing as the lookback periods decrease. An example of a direct implication for event trading is when shock is forecasted but its timing uncertain, equidistant sampling produces a lower error on the true maximum than random choice. We conclude that the instrument provides an ideal space for investors to balance their risk, and as a prime candidate to hedge extreme volatility. We discuss the application of the amnesiac lookback option and path-dependent options to cryptocurrencies and blockchain commodities in the context of smart contracts.
This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.
This study back-tests a marginal cost of production model proposed to value\nthe digital currency bitcoin. Results from both conventional regression and\nvector autoregression (VAR) models show that the marginal cost of production\nplays an important role in explaining bitcoin prices, challenging recent\nallegations that bitcoins are essentially worthless. Even with markets pricing\nbitcoin in the thousands of dollars each, the valuation model seems robust. The\ndata show that a price bubble that began in the Fall of 2017 resolved itself in\nearly 2018, converging with the marginal cost model. This suggests that while\nbubbles may appear in the bitcoin market, prices will tend to this bound and\nnot collapse to zero.\n
Alexandre Bovet, Carlo Campajola, Jorge F. Lazo, Francesco Mottes · 10 authors
The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions.
Recently, the notion of cryptocurrencies has come to the fore of public interest. These assets that exist only in electronic form, with no underlying value, offer the owners some protection from tracking or seizure by government or creditors. We model these assets from the perspective of asset flow equations developed by Caginalp and Balenovich, and investigate their stability under various parameters, as classical finance methodology is inapplicable. By utilizing the concept of liquidity price and analyzing stability of the resulting system of ordinary differential equations, we obtain conditions under which the system is linearly stable. We find that trend-based motivations and additional liquidity arising from an uptrend are destabilizing forces, while anchoring through value assumed to be fairly recent price history tends to be stabilizing.
Tianyu Ray Li, Anup S. Chamrajnagar, Xander R. Fong, Nicholas R. Rizik · 5 authors
In this paper, we analyze Twitter signals as a medium for user sentiment to predict the price fluctuations of a small-cap alternative cryptocurrency called \emph{ZClassic}. We extracted tweets on an hourly basis for a period of 3.5 weeks, classifying each tweet as positive, neutral, or negative. We then compiled these tweets into an hourly sentiment index, creating an unweighted and weighted index, with the latter giving larger weight to retweets. These two indices, alongside the raw summations of positive, negative, and neutral sentiment were juxtaposed to $\sim 400$ data points of hourly pricing data to train an Extreme Gradient Boosting Regression Tree Model. Price predictions produced from this model were compared to historical price data, with the resulting predictions having a 0.81 correlation with the testing data. Our model's predictive data yielded statistical significance at the $p < 0.0001$ level. Our model is the first academic proof of concept that social media platforms such as Twitter can serve as powerful social signals for predicting price movements in the highly speculative alternative cryptocurrency, or ``alt-coin'', market.
The attempts to create an adequate model of socio-economic critical events, which, as it has been historically proven, are almost permanent, were, are and will always be made. Actually, it is a supertask, impossible to solve. However, the potentially useful solutions, local in time or other socio-economic logistic coordinates, are possible. In fact, they have to be the object of interest for a real and effective economic science. Econophysics is a young interdisciplinary scientific field, which developed and acquired its name at the end of the last century. Quantum econophysics, a direction distinguished by the use of mathematical apparatus of quantum mechanics as well as its fundamental conceptual ideas and relativistic aspects, developed within its boundaries just a couple of years later, in the first decade of the 21-st century.
Abstract Cryptocurrencies have experienced an exponential growth trend in the past 24 months, followed by a big crash. In the early years of the Internet, inspired entrepreneurs such as Jeffrey Bezos realized that, when something grows exponentially, it becomes ubiquitous within a short time span. Similarly to the Internet in 1994, cryptocurrencies have recently been growing at a dazzling rate, thus one can expect them to be used on a global scale very soon, in spite of the last bubble which has already burst. Alternative currencies are greeted with great enthusiasm, due to their potential to return financial power back to the people, especially in the context of general dissatisfaction and disappointment with the banking sector. They bring about several advantages, such as financial innovations, lower fees as well as increased availability to developing populations. At the same time, their high volatility and lack of supervision might imply that they only serve as complementary financing and not as a substitute of traditional banking. This article discusses the development of cryptocurrencies, including aspects related to Bitcoin, financial technology and the blockchain. Using historical data from Coinmarketcap.com between April 2013 and February 2018, I run a quantitative analysis of the distributions and evolution over time for all listed cryptocurrencies with known market capitalization. I look at the interplay between number of cryptocurrencies and market value, at growth rates, cumulative shares and volatility. I find a phenomenon of exponential growth and violent volatility, which I explain in light of cryptocurrencies’ strengths and weaknesses, as identified in the literature. I emphasize the importance of cryptocurrencies in the context of the global digital economy and I discuss future implications.
Dionysios S. Demetis, Michael Mainelli, Matthew Leitch
Cryptocurrencies have the potential to become effective currencies that give a higher level of macroeconomic control, thanks to the information that is available about holdings and transactions, and the potential for automated control mechanisms. However, these cryptocurrencies need to be designed properly and tested before launch. This paper reports the early results of an economic model that simulates a variety of behaviors by economic agents and some simple control mechanisms. An economic simulation model is likely to be a valuable tool in developing effective cryptocurrency systems and interacting with regulators.
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
In this paper, by using econometric techniques we provide evidence that bitcoin exhibited the formation of speculative bubble in 2017. To conceptually rationalize the results, we delve into the extant theoretical approaches developed by Kindleberger's (1978) speculative bubbles and Minsky's (1992) financial instability hypothesis. Certainly, bitcoin has spurred a revolution in payment technology that, if treated cautiously can facilitate financial intermediation and inclusion. Ultimately, whether or not bitcoin constitutes a bubble is a decision for investors as the road to hell is paved with good promises.
Stanisław Drożdż, Robert Gȩbarowski, Ludovico Minati, Paweł Oświȩcimka · 5 authors
Based on 1-minute price changes recorded since year 2012, the fluctuation properties of the rapidly-emerging Bitcoin (BTC) market are assessed over chosen sub-periods, in terms of return distributions, volatility autocorrelation, Hurst exponents and multiscaling effects. The findings are compared to the stylized facts of mature world markets. While early trading was affected by system-specific irregularities, it is found that over the months preceding Apr 2018 all these statistical indicators approach the features hallmarking maturity. This can be taken as an indication that the Bitcoin market, and possibly other cryptocurrencies, carry concrete potential of imminently becoming a regular market, alternative to the foreign exchange (Forex). Since high-frequency price data are available since the beginning of trading, the Bitcoin offers a unique window into the statistical characteristics of a market maturation trajectory.