In a novel model of an endowment economy, we analyze coexistence and competition between traditional fiat money (Dollar) and another intrinsically worthless medium of exchange, not controlled by a central bank, such as Bitcoin. Agents can trade consumption goods in either currency or hold on to currency for speculative purposes. A central bank ensures a Dollar inflation target, while Bitcoin mining is decentralized via proof-of-work. We analyze Bitcoin price evolution and interaction between the Bitcoin price and monetary policy which targets the Dollar. We obtain a fundamental pricing equation, which in its simplest form implies that Bitcoin prices form a martingale. We derive conditions, under which Bitcoin speculation cannot happen, and the fundamental pricing equation must hold. We show that the block rewards are not a tax on Bitcoin holders: they are financed by Dollar taxes imposed by the Dollar central bank. We discuss monetary policy implications and characterize the range of equilibria.
Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more di?erent. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and ?nd di?erences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in ?nancial markets with ?ight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and ?nd no evidence for hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally di?erent properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not re?ect any distinctive properties of Gold other than asymmetric response in variance.
ABSTRACT This paper investigates whether Tether, a digital currency pegged to the U.S. dollar, influenced Bitcoin and other cryptocurrency prices during the 2017 boom. Using algorithms to analyze blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. The flow is attributable to one entity, clusters below round prices, induces asymmetric autocorrelations in Bitcoin, and suggests insufficient Tether reserves before month‐ends. Rather than demand from cash investors, these patterns are most consistent with the supply‐based hypothesis of unbacked digital money inflating cryptocurrency prices.
This paper examines the movement of cryptocurrencies’ return based on price. This volatility can spread to others of the same kind. Currently, the more cryptocurrencies are traded in market, the more chances are available for investors. The author wonders whether contagion risk among these cryptocurrencies happens or not in the event of crashing. We also introduce one empirical evidence of the mutual influence on these cryptocurrencies using Copulas approach. The findings show that all pairs have the structure dependence with Kendall-plots, particularly strong left tail dependence with Chi-plots. It also means the existence of contagion risk among these cryptocurrencies. The three methodologies namely Kendall-plots, Chi-plots and Copulas estimation produce consistent results. Therefore, the investors should carefully perform portfolio diversification to avoid contagious phenomenon.
The emergence of Bitcoin in 2009 has received considerable attention surrounding the validity of cryptocurrencies as a viable and, in some jurisdictions, a legal currency alternative. Despite widespread concern that these cryptocurrencies are fostering the environment within which a substantial bubble can occur, it is important to analyze whether these new assets are behaving similarly to major international currencies. This paper investigates the effects of international monetary policy changes on bitcoin returns using a GARCH (1.1) estimation model. The results indicate that monetary policy decisions based on interest rates taken by the Federal Open Market Committee in the United States significantly impact upon bitcoin returns. After controlling for international effects, we find significant evidence of volatility effects driven by United States, European Union, United Kingdom and Japanese quantitative easing announcements. These results show that, despite its nature and ideals, bitcoin seems to be subject to the same economic factors as traditional fiat currencies, and is not entirely unaffected by government policies. This result has implications for investors using bitcoin as a hedging or diversification tool. In addition, we contribute to the existing debate regarding the classification of bitcoin as an asset class, by illustrating that bitcoin volatility exhibits various reactions that bear resemblance to both currency pairs and store-of-value assets.
We analyse the Tangle --- a DAG-valued stochastic process where new vertices get attached to the graph at Poissonian times, and the attachment's locations are chosen by means of random walks on that graph. These new vertices, also thought of as "transactions", are issued by many players (which are the nodes of the network), independently. The main application of this model is that it is used as a base for the IOTA cryptocurrency system (www.iota.org). We prove existence of "almost symmetric" Nash equilibria for the system where a part of players tries to optimize their attachment strategies. Then, we also present simulations that show that the "selfish" players will nevertheless cooperate with the network by choosing attachment strategies that are similar to the "recommended" one.
This paper studies the liquidity of Bitcoin using the time series daily data over the period 1/1/2014 to 12/31/2015. Based on the available data for Bitcoin, five liquidity measures are chosen to compare the liquidities among five Bitcoin exchanges and the liquidities of different sizes of stocks. The results suggest that the liquidity of Bitcoin depends on the choice of the Bitcoin exchanges, and Bitfinex, one of the Bitcoin exchanges, provides the highest liquidity for Bitcoin trading. Moreover, the results indicate that, on average, stocks are more liquid than Bitcoin.
This paper examines the comprehensive idea about the growth and future sustainability of bitcoin as a cryptocurrency. The transaction volume of bitcoin is used as the growth of the bitcoin and the bitcoin log return is used for testing the volatility which is helpful for the future sustainability of bitcoin. The study period says that the growth of bitcoin’s transaction volume is an increasing trend as more day to day transaction is minting with the exchange of Bitcoin. The study also uses ARCH & GARCH methodology to know the volatility of this emerging digital currency, and the GARCH result shows that it is a highly volatile currency. As a result, most of the governments have not given their legal status for the use of bitcoin in their country. But if bitcoin will be stable in the future, then it is easily accepted through worldwide and in the long run, people will have more faith in the cryptocurrency technology and its usability.
Leonardo Ermann, Klaus M. Frahm, Dima L. Shepelyansky
We construct and study the Google matrix of Bitcoin transactions during the time period from the very beginning in 2009 till April 2013. The Bitcoin network has up to a few millions of bitcoin users and we present its main characteristics including the PageRank and CheiRank probability distributions, the spectrum of eigenvalues of Google matrix and related eigenvectors. We find that the spectrum has an unusual circle-type structure which we attribute to existing hidden communities of nodes linked between their members. We show that the Gini coefficient of the transactions for the whole period is close to unity showing that the main part of wealth of the network is captured by a small fraction of users.
Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
Oct 1, 2017·2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), Kharkov, Ukraine, 2017, pp. 456-459
In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community activities was detected. Time series fractal analysis indicated the presence of self-similar and multifractal properties. The results of researches showed that the series having a strong correlation dependence have a similar multifractal structure.
The 2008 financial crisis had scattered incredulity around the globe regarding traditional financial systems, which made investors and non-financial customers turn to other alternative such as digital banking systems. The existence and development of blockchain technology make cryptocurrency in recent years believably become a complete alternative to traditional ones. Bitcoin is the world's first peer-to-peer and decentralized digital cash system initiated by Nakamoto [1]. Though being the most prominent cryptocurrency, Bitcoin has not been a legal trading currency in various countries. Its exchange rate has appeared to be an exceptionally high-risk portfolio with extreme volatility, which requires a more detailed evaluation before making any decision. This paper utilizes knowledge of statistics for financial time series and machine learning to (i) fit the parametric distribution and (ii) model and forecast the volatility of Bitcoin returns, and (iii) analyze its correlation to other financial market indicators. The fitted parametric time series model significantly outperforms other standard models in explaining the stylized facts and statistical variances in the behavior of Bitcoin returns. The model forecast also outperforms some machine learning methodologies, which would benefit policy makers, banks and financial investors in trading activities for both long-term and short-term strategies.
Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.
This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility.
Although Bitcoin has long been dominant in the crypto scene, it is certainly not alone. Ether is another cryptocurrency related project that has attracted an intensive attention because of its additional features. This study seeks to test whether these cryptocurrencies differ in terms of their volatile and speculative behaviors, hedge, safe haven and risk diversification properties. Using different econometric techniques, we show that a) Bitcoin and Ether are volatile and relatively more responsive to bad news, but the volatility of Ether is more persistent than that of Bitcoin; b) for both cryptocurrencies, the exuberance and the collapse of bubbles were identified, but Bitcoin appears more speculative than Ether; c) there is negative and significant correlation between Bitcoin/Ether and other assets (S\&P500 stocks, US bonds, oil), which would indicate that digital currencies can hedge against the price movements of these assets; d) there is negative tail independence between Bitcoin/Ether and other financial assets, implying that these cryptocurrencies exhibit the function of a weak safe haven; and e) The inclusion of Bitcoin/ Ether in a portfolio improve its efficiency in terms of higher reward-to-risk ratios. But investors who hold diversified portfolios made of stocks or bonds and Ether may face losses over bearish regime. In such situation, stock and bond investors may take a short position on Bitcoin.
Using 1-min returns of Bitcoin prices, we investigate statistical properties and multifractality of a Bitcoin time series. We find that the 1-min return distribution is fat-tailed, and kurtosis largely deviates from the Gaussian expectation. Although for large sampling periods, kurtosis is anticipated to approach the Gaussian expectation, we find that convergence to that is very slow. Skewness is found to be negative at time scales shorter than one day and becomes consistent with zero at time scales longer than about one week. We also investigate daily volatility-asymmetry by using GARCH, GJR, and RGARCH models, and find no evidence of it. On exploring multifractality using multifractal detrended fluctuation analysis, we find that the Bitcoin time series exhibits multifractality. The sources of multifractality are investigated, confirming that both temporal correlation and the fat-tailed distribution contribute to it. The influence of "Brexit" on June 23, 2016 to GBP--USD exchange rate and Bitcoin is examined in multifractal properties. We find that, while Brexit influenced the GBP--USD exchange rate, Bitcoin was robust to Brexit.
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent $H>0.5$, is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with $10^4$ geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferences is 10\%. The most probable Bitcoin price at the beginning of 2018 is 6358 USD.
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst\nexponent $H>0.5$, is exploited in order to predict future BTC/USD price. A\nMonte Carlo simulation with $10^4$ geometric fractional Brownian motion\nrealisations is performed as extensions of historical data. The accuracy of\nstatistical inferences is 10\\%. The most probable Bitcoin price at the\nbeginning of 2018 is 6358 USD.\n
Currently, there is no consensus on the real properties of Bitcoin. The\ndiscussion comprises its use as a speculative or safe haven assets, while other\nauthors argue that the augmented attractiveness could end accomplishing money's\nfunctions that economic theory demands. This paper explores the association\nbetween Bitcoin's market price and a set of internal and external factors using\nBayesian Structural Time Series Approach. I aim to contribute to the discussion\nby differentiating among several attractiveness sources and employing a method\nthat provides a more flexible analytic framework that decompose each of the\ncomponents of the time series, apply variable selection, include information on\nprevious studies, and dynamically examine the behavior of the explanatory\nvariables, all in a transparent and tractable setting. The results show that\nthe Bitcoin price is negatively associated with a neutral investor's sentiment,\ngold's price and Yuan to USD exchange rate, while positively related to stock\nmarket index, USD to Euro exchange rate and variated signs among the different\ncountries' search trends. Hence, I find that Bitcoin has mixed properties since\nstill seems to act as a speculative, safe haven and a potential a capital\nflights instrument.\n
This article demonstrates the possibility of constructing indicators of critical and crisis phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex systems such as recurrent analysis of dynamic systems and the calculation of permutation entropy are used. It is shown that it is possible to construct dynamic measures of complexity, both recurrent and entropy, which behave in a proper way during actual pre-crisis periods. This fact is used to build predictors of crisis phenomena on the example of the main five crises recorded in the time series of the key cryptocurrency bitcoin, the effectiveness of the proposed indicators-precursors of crises has been identified.