Марат Рашитович Сафиуллин, A.A. Abdukaeva, Leonid Alekseevich Elshin
The accelerated pace of development of the cryptocurrency market and its integration into the system of economic, operational, financial and other processes determines the need for a comprehensive study of this phenomenon. This is particularly relevant because in recent months, at the state level have intensified discussions on the prospects of the legalization of the cryptocurrency market and the possibility of using its tools in the economic activities of economic agents. Despite the sometimes polar views and approaches at the moment among Russian experts regarding the solution to this issue, the development of the crypto-currencies market is extremely high, regardless of its regulation. This determines and actualizes the scientific research in the field of evaluation of the prospects of development of this market, forming the subject of this study in order to predict the possible effects and risks for the national economic system. The purpose of the article is the development of tools of modelling and forecasting the volatility of the cryptocurrency market on the basis of “foreseeing” fluctuations in the value of “digital money” using special models of autoregression (ARMA, ARIMA). The study was based on the application of a class of parametric models. It allowed describing both stationary and non-stationary time series and on this basis to develop a system of prognostic estimates for the prospects of further development of the series under study. With the help of our ARIMA model, which evaluates the parameters of the analyzed time series of the cryptocurrency exchange rate, we developed a system of prognostic assessments for the short term. The authors proved that the application of such models with a high level of reliability predicts future adjustments in the market under study. It leads to a high level of prospects for their use in modelling future parameters of the cryptocurrency market development. This creates a basis for a business to develop adaptive mechanisms for to emerging price index adjustments of “digital money”.
The issue of market efficiency for cryptocurrency exchanges has been largely unexplored. Here we put Bitcoin, the leading cryptocurrency, on a test by studying the applicability of the Efficient Market Hypothesis by Fama from two viewpoints: (1) the existence of profitable arbitrage spread among Bitcoin exchanges, and (2) the possibility to predict Bitcoin prices in EUR (time period 2013-2017) and the direction of price movement (up or down) on the daily trading scale. Our results show that the Bitcoin market in the time period studied is partially inefficient. Thus the market process is predictable to a degree, hence not a pure martingale. In particular, the F-measure for XBTEUR time series obtained by three major recurrent neural network based machine learning methods was about 67%, i.e. a way above the unbiased coin tossing odds of 50% equal chance.
The bitcoin price has surged in recent years and it has also exhibited phases of rapid decay. In this paper we address the question to what extent this novel cryptocurrency market can be viewed as a classic or semi-efficient market. Novel and robust tools for estimation of multi-fractal properties are used to show that the bitcoin price exhibits a very interesting multi-scale correlation structure. This structure can be described by a power-law behavior of the variances of the returns as functions of time increments and it can be characterized by two parameters, the volatility and the Hurst exponent. These power-law parameters, however, vary in time. A new notion of generalized Hurst exponent is introduced which allows us to check if the multi-fractal character of the underlying signal is well captured. It is moreover shown how the monitoring of the power-law parameters can be used to identify regime shifts for the bitcoin price. A novel technique for identifying the regimes switches based on a goodness of fit of the local power-law parameters is presented. It automatically detects dates associated with some known events in the bitcoin market place. A very surprising result is moreover that, despite the wild ride of the bitcoin price in recent years and its multi-fractal and non-stationary character, this price has both local power-law behaviors and a very orderly correlation structure when it is observed on its entire period of existence.
The Bitcoin protocol prevents the occurrence of double-spending (DS), i.e. the utilization of the same currency unit more than once. At the same time a DS attack, where more conflicting transactions are generated, might be performed to defraud a user, e.g. a merchant. Therefore, in this work, we propose a model for detecting the presence of conflicting transactions by means of an 'oracle' that polls a subset of nodes of the Bitcoin network. We assume that the latter has a complex structure. So, we investigate the relation between the topology of several complex networks and the optimal amount, and distribution, of a subset of nodes chosen by the oracle for polling. Results show that small-world networks require to poll a smaller amount of nodes than regular networks. In addition, in random topologies, a small number of polled nodes can make a detection system fast and reliable even if the underlying network grows.
Nino Antulov-Fantulin, Dijana Tolić, Matija Piškorec, Ce Zhang · 5 authors
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.
In recent years, there have been many significant changes in commercial transactions. Not only e-commerce continues to grow, but also the form of payment services and service providers are consistently growing, such as virtual currency. One of virtual currency that quite popular is cryptocurrency especially Bitcoin. China became the country with the largest Bitcoin market in the world in the past few years. However, due to the concerns about money laundering and threats to China's financial stability and affecting the domestic currency, the Chinese government has formed a strict policy on Bitcoin. Therefore, nowadays, China is no longer the largest Bitcoin market in the world. Regarding the recently implemented policy, this study aims to analyze whether Bitcoin does affect China's exchange rate. The main independent variables in this research are specified to Bitcoin price volatility from BTCE, and controlled with the variable of the current account, inflation, and money supply. Monthly time series data from November 2012 until July 2017 is analyzed using autoregressive distributed lag (ARDL). The estimation results show that Bitcoin price volatility significantly affects the exchange rate in the long run. The higher of Bitcoin price volatility implies higher risk. The negative sign in the coefficient suggests that when Bitcoin's price volatility increases, investors tend to switch their investments on real currency will be preferable so that the exchange rate will be appreciated.
We investigate connectedness within and across two major groups or assets: i) five popular cryptocurrencies, and ii) six major asset classes plus two commonly employed risk factors. Granger-causality tests uncover six direct channels of causality from the elements of the mainstream assets/risk factors group to digital assets. On the other hand there are two statistically significant causal links going in the other direction. In order to provide some perspective on the magnitude of the uncovered linkages we supplement the analysis by estimating networks from forecast error variance decompositions. The estimated connectedness within the groups is relatively large, whereas the linkages across the two groups are small in comparison. Namely, less than 2.2 percent of future uncertainty of any cryptocurrency is sourced from all non-crypto assets combined, while the joint contribution of all digital assets to non-crypto uncertainty does not exceed 1.5 percent.
This article deals with the concepts of social currencies and cryptocurrencies. The objective of the present paper is to identify similarities and differences between to two currency systems which represent a new generation of money that exists alongside the official and legal money system. The paper includes an analysis of the major characteristics of both currencies, their operating mechanisms in global and local contexts, as well as their risks and challenges for the financial markets. The article uses a mainly documentary research method and presents selected contributions of experts on the topics of social currencies and cryptocurrencies. Furthermore, empirical evidence is presented to highlight some important characteristics of the Bitcoin currency. The principal result of the paper is that, indeed there exist similarities between social currencies and cryptocurrencies, as for example the absence of a central bank, a lack of regulation and a limited minting process. However, because of aspects like their different origins, their local vs. global character and their inherent financial risks, the two money systems need to be interpreted as fundamentally different. Especially with reference to globally operating cryptocurrencies, given that there does not exist any public cover of the currency nor sufficient regulation, risk management mechanisms need to be improved in order to diminish the speculative tendencies inherent to this currency.
Marian Gidea, Daniel Goldsmith, Yuri A. Katz, Pablo Roldan · 5 authors
We analyze the time series of four major cryptocurrencies (Bitcoin, Ethereum,\nLitecoin, and Ripple) before the digital market crash at the end of 2017 -\nbeginning 2018. We introduce a methodology that combines topological data\nanalysis with a machine learning technique -- $k$-means clustering -- in order\nto automatically recognize the emerging chaotic regime in a complex system\napproaching a critical transition. We first test our methodology on the complex\nsystem dynamics of a Lorenz-type attractor, and then we apply it to the four\nmajor cryptocurrencies. We find early warning signals for critical transitions\nin the cryptocurrency markets, even though the relevant time series exhibit a\nhighly erratic behavior.\n
Pilar Grau Carles, Diego Jaureguizar Arellano, Carlos Jaureguizar Francés
In this paper we examine the characteristics of the daily price series of 16 different cryptocurrencies between July 2017 and February 2018. The methodologies used for the analysis are the so-called Minimum Spanning Tree (MST) and hierarchical analysis by dendrogram, both obtained Pearson correlations between daily returns. This methodology visualizes the market relationships between the assets analyzed, identifying a high correlation between price movements for all the currencies. In addition, it has been possible to identify Ethereum’s position as a benchmark currency in the cryptocurrency market, rather than Bitcoin, as one might expect, due to its popularity and trading volume.
Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.
Libing Fang, Elie Bouri, Rangan Gupta, David Roubaud
We assess whether the long-run volatilities of Bitcoin, global equities, commodities, and bonds are affected by global economic policy uncertainty. Empirical results provide evidence supporting that, except for the case of bonds. We further examine whether the correlation between Bitcoin and global equities, commodities, and bonds are affected by global economic policy uncertainty and the results reveal that global economic policy uncertainty has a negative significant impact on the Bitcoin-bonds correlation, and a positive impact on both Bitcoin-equities and Bitcoin-commodities correlations, suggesting a possibility for Bitcoin to act as a hedge under specific economic uncertainty conditions. Interestingly, the hedging effectiveness of Bitcoin for both global equities and global bonds enhances slightly after considering the level of global economic policy uncertainty. Implications for investors and policy-makers are discussed.
Abstract We introduce the distributed ledger (blockchain) technology of crypto‐currencies. We examine the ‘monetary’ attributes of crypto‐currencies, and describe some of the reasons they have been adopted. The paper discusses the mechanics of Bitcoin – the original crypto‐currency – to illustrate the fundamental elements of decentralized crypto‐currencies. We then provide a high‐level summary of the implications of crypto‐currencies for consumers, financial systems, and for monetary and regulatory authorities. We argue that crypto‐currencies are unlikely to supplant traditional fiat currencies and we anticipate an enduring role for financial intermediaries in facilitating credit.