Cryptocurrency Exchange Rate Research Articles

A decision support software system for cryptocurrency traders on the Trading View platform

The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi-criteria analysis model in the process of supporting decision-making for cryptocurrency trading. A software decision support system for cryptocurrency traders on the Trading View platform has also been developed, which allows the cryptocurrency trader to get the value of the current situation of the cryptocurrency market in the form of a value using the method of weighting coefficients and selected indicators. Among the selected indicators: RSI, MA, CCI, Stochastic Oscillator, OBV, ADX, CMF to determine the moment of opening a position, and Fibonacci Retracement, Ichimoku Cloud to determine the closing of positions. Taking into account all the indicators and the coefficients determined for them, the obtained range of values is from 0 % to 100 %. If the value of the indicator exceeds the threshold of 20 %, it means that it is necessary to inform the trader about a possible entry point. That is, a value of 20 % to 40 % is weak performance, 40 % to 60 % is medium performance, 60 % to 80 % is strong performance, and a value greater than 80 % will not be overlapped by new pyramiding values for a better overall indicator success rate. The value of the indicator determines the potential effectiveness of opening positions, and thanks to the RSI indicator, the direction of opening positions is determined. The direction of the position is divided into long and short. An indicator has been developed for the TradingView platform, which, unlike existing simple indicators, collects data from open access and calculates a potential point for opening a position. Obtaining the numerical value of a single indicator saves the trader time to review and analyze a collection of indicators and time to decide on opening a position, as the cryptocurrency market is known for its sudden volatility, where a decision must be made quickly.

On modeling the log-returns of Bitcoin and Ethereum prices against the USA Dollar

The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.

Forecasting the cryptocurrency exchange rate based on the ranking of expert opinions

To date, most existing cryptocurrency exchanges do not have in their arsenal tools that would allow them to verify and investigate the information disseminated on social networks regarding a particular cryptocurrency. This makes it possible to conduct a relevant research with the subsequent development of a tool that, if used correctly, will provide users with advisory advice on further actions in relation to the cryptocurrency under study in the system. Based on this advice, interested parties will be able to adjust their decisions regarding further financial steps. The basis of most recommender systems is always the need to identify some influencing factors, which are later given certain weights to facilitate and simplify the formulation of further advice for users. In this paper, we study the influence of celebrity publications on the formation of prices for a particular cryptocurrency at a certain point in time. The importance and existence of this influence was previously proven by statistical methods. The purpose of the study is to develop an algorithm for studying the level of influence of posts of each of the selected group of experts in social networks on the cryptocurrency rate. The object of the study is the forecast of cryptocurrency rates. The input data used were the list of experts whose level of influence will be studied, the time interval of the study, the number of posts made by each of the experts in question over the specified period of time, and the actual cryptocurrency rates for the relevant period. The experts were well-known personalities who are either knowledgeable in the field of finance in general and cryptocurrencies in particular, or whose activities are somehow related to a particular cryptocurrency. Research methods. Experts are ranked based on the full probability and Bayesian formulas. Forecasting of cryptocurrency rates in a selected period of time is carried out using the algorithm for forecasting cryptocurrency rates based on expert posts on social networks (AUDSM). To control the accuracy of forecasts, the relative average error is calculated. Recommendations for financial transactions with cryptocurrencies are formed by entering the critical value of the exchange rate and calculating the arithmetic mean of cryptocurrency exchange rates for a specified period of time. Results. As a result of the research, an algorithm has been developed that allows taking into account the impact of the posts of each of the selected ranked group of experts on changes in the rates of a particular cryptocurrency. On the basis of the obtained forecasts, the paper presents a methodology for forming recommendations for financial transactions with them.

Analysis of approaches and their features to research the dynamics of cryptocurrencies

The purpose of the study is to provide a comparative analysis of approaches to analyzing the trends in cryptocurrency dynamics. The paper analyzes the trends in cryptocurrency development, which has shown an increase in the influence of cryptocurrency on the structure of the financial market. It has been determined that from 2013 to 2023, the capitalization of cryptocurrency market increased almost 1 000 times. However, in recent years, the number of "fake" cryptocurrencies has also increased, so the total number of cryptocurrencies has almost not changed in the last two years. The works of researchers on the analysis of the trends in cryptocurrency exchange rates have been studied, and three main approaches to the analysis have been formed. The main components of the first approach have been investigated, the influence of miners, mining costs, blockchains, and the interaction of mining participants on the formation of exchange rates has been determined. It is found that the necessity to analyze the cryptocurrency market in conjunction with other elements of the financial market is the key aspect of the second approach to the study of trends in the exchange rate of cryptocurrencies. In this approach, cryptocurrency acts as an alternative to centralized components of the financial market and as an element of financial freedom. The study of the works of the third approach allows to identify the main methods and models for analyzing the dynamics of exchange rates, among which the main place is occupied by: models of time series analysis taking into account sentiments (Sentiment-Enriched Time Series Forecasting – SETS models), deep learning models for forecasting of processes with long and short-term memory, recurrent neural networks, and gated recurrent unit models

Програмна експертна система вибору алгоритмів прогнозування криптовалюти у режимі реального часу

Introduction. Today there is a large number of cryptocurrencies in the world with their own unique characteristics. Traders and investors working in the cryptocurrency market use various software to analyze and forecast the exchange rate of cryptocurrencies. The ability to correctly and quickly make decisions using the results of cryptocurrency rate forecasting is currently defined as a key goal for traders, investors and analysts of this market. Purpose. In this article, the existing methods of analysis and forecasting of the exchange rate on cryptocurrency exchanges were considered, namely – fundamental and technical analysis, machine learning, news flow analysis, and a hybrid approach. During the study, it was demonstrated that different algorithms can have a high level of probability of predictions in a specific situation and with optimized parameters, but at the same time show much lower probability indicators with the slightest change in parameters or dynamics of the cryptocurrency exchange rate. Accordingly, a perfect method with universal parameters that will always consistently show high results in the probability of predictions does not exist. The implementation of a software automatic expert system for choosing algorithms in real-time will allow automatically choose the best algorithm for cryptocurrency forecasting based on the analysis of the effectiveness of algorithms over the last N iterations in the past. Methods. By the development of automatic expert system for choosing cryptocurrency forecasting algorithms in real-time. Results. The process of analyzing and forecasting the cryptocurrency exchange rates with an existing software expert system for choosing algorithms for cryptocurrency forecasting is considered, and the logic of the operation of such an expert system is presented. Also, ways to solve problems that may arise during the application of this system were identified and substantiated. Conclusion. The alternative approach to the analysis and forecasting of cryptocurrencies in real-time was implemented in the software expert system for choosing alghorithms, which allows stabilizing the high probability of the exchange rate forecasts on the cryptocurrency market in comparison with the use of only one method or the hybridization of several methods.

CRYPTOCURRENCY: FEATURES AND PROSPECTS OF DEVELOPMENT

Introduction. The development of information technologies led to the emergence of new financial instruments, payment systems, methods and payment platforms. Cryptocurrency is a means of circulation and payments fundamentally different from traditional ones. Awareness of the emergence and existence of electronic money is quite high, however, awareness of the mechanism of its functioning and the possibility of using/making calculations, financing projects is low. The puppose of the article is to highlight the main characteristic features of cryptocurrencies, to justify the advantages and disadvantages of cryptocurrencies, to analyze the state, dynamics and development prospects of the cryptocurrency market. Results. Characteristic features of cryptocurrencies include: lack of payment system administration; impossibility of forced return, withdrawal or "freezing" of payments (without access to the owner's private key). Cryptocurrency is an investment object, and the speculative-investor type of demand for cryptocurrencies is also noted. The authors identified the advantages of using cryptocurrencies, including: equal conditions of use by users, unlimited possibilities of transactions, their transparency, lack of complications in international transfers and the impossibility of blocking or freezing a cryptocurrency wallet and its funds, no commission, open code of cryptocurrency, decentralization. Among the disadvantages of cryptocurrencies are identified: the danger of losing the key to cryptocurrency; the difficulty of returning funds in case of erroneous currency transfer; lack of guarantees for keeping electronic wallets; intervention by hackers; instability of the cryptocurrency exchange rate, dependence of the exchange rate on demand; high energy consumption; distrust of users. The dynamics of the exchange rate of bitcoin and ethereum were analyzed, significant fluctuations in their exchange rate relative to the dollar during the research period were noted. The difference in the views of countries on the issue of regulating the circulation of cryptocurrencies is described (recognition of operations with cryptocurrency as illegal activity, full / partial ban, legalization of legal circulation of electronic money). Conclusions. Barriers and prospects for the development of the electronic money market have been identified. Given the described features of cryptocurrencies, the prospects of recognition / settlement of cryptocurrencies as a financial instrument at the same level as freely convertible currencies (dollar, euro, etc.) are unrealistic and impractical within the framework of the existing mechanisms of functioning of the cryptocurrency market. The prospects for the growth of cryptocurrency circulation in the sectors of NFT-tokens (in games, music, art, sports) were noted.

Wavelet analysis and forecasting of financial time series

Models and methods of forecasting financial time series are considered in the work. The main advantages and disadvantages of traditional models and neural networks for forecasting without data preprocessing are analyzed. Wavelet analysis and a recurrent neural network with long short-term memory (LSTM) were applied to predict the exchange rate of cryptocurrency. The obtained results are compared with the results of existing approaches, the efficiency is determined and a solution is proposed. Figs.: 6. Tabl.: 2. Refs.: 13 titles.
 Keywords: financial time series, neural networks, forecasting, data preprocessing, wavelet analysis, recurrent neural network with long short-term memory, cryptocurrency exchange rate.

Statistical properties of exchange rates of cryptocurrencies and their market dynamics as compared to selected fiat currencies

The development of cryptocurrencies was the consequence of the endeavours of some circles of computer scientists associated with anarchist views to create a medium of exchange that would be decentralised, independent from institutions creating monetary policy, governments and politicians, and whose functioning would not require any clearing institutions. The first cryptocurrency that fulfilled most of these postulates was Bitcoin. In a relatively short time, it became very popular among investors and reached a significant capitalisation on financial markets, but soon became an object of speculation rather than a medium of exchange. So far, no common view has been established as to whether Bitcoin is closer to being money, an object of speculation or an investment asset. The question what Bitcoin really is has fundamental importance not only to the entire cryptocurrency project, but also to the economy in general. The purpose of the study discussed in the article was to compare the statistical properties of cryptocurrency exchange rates (on the example of Bitcoin) with such properties of the exchange rates of fiat currencies, and to assess whether investments on the Bitcoin market can be considered similar to investments on fiat currency markets in terms of some analysed features. Basic statistical characteristics of the relative increments in the exchange rates of Bitcoin and four fiat currencies paired with the US dollar were analysed, as were some selected properties of stochastic processes that could be used to model the dynamics of those increments. In addition, a theoretical analysis of both Bitcoin and some other cryptoassets was carried out, which explained, among other things, why the study compared Bitcoin with currencies and not with investment assets. The research demonstrated that there are differences between several statistical properties of Bitcoin and those of fiat currencies. In addition to the generally higher unconditional volatility, differences were observed between the trends in exchange rates of Bitcoin and fiat currencies versus the US dollar, the levels of relative increments in the exchange rates, the conditional volatility, and the residual distributions.

How many neurons are needed to make a short-term prediction of the Bitcoin exchange rate?

The goal of our work was to select a neural network architecture that would give the best prediction of the Bitcoin exchange rate using historical data. Our work fits into the very important topic of predicting the value of the cryptocurrency exchange rate, and makes use of recent data which, as a result of the high Bitcoin exchange rate dynamics of the last year, differs significantly from those of previous years. We propose and test a number of neural network-based architectures and conduct a discussion of the results. Unlike previous state-of-the-art works, we conducted a comprehensive comparison of three different neural network-based models: MLP (multilayer perceptron), LSTM (long short-term memory) and CNN (convolutional neural network). We tested them for a wide range of parameters. The results we present are, to the best of our knowledge, the most up to date when it comes to the application of artificial intelligence methods for the prediction of cryptocurrency exchange rates. The best-performing architectures were used for a website that gives real-time predictions of the Bitcoin exchange rate. The website is available at http://stpbtc-ii.up.krakow.pl/. Source codes of our research are available to download in order to make our experiment reproducible.

Analysis of cryptocurrency exchange rates vs USA dollars using a new Dagum model

This paper offers the introduction of a new updated form of the Dagum distribution. The new updated form of the Dagum model is called a novel generalized-Dagum distribution. The proposed novel generalized-Dagum distribution is a prominent updated form of the Dagum model with a single additional/extra parameter. The novel generalized-Dagum model is produced by mixing the Dagum distribution with the novel generalized-M distributions approach. The heavy-tailed properties of the novel generalized-Dagum model are obtained. The derivation of the estimators and a simulation study of the novel generalized-Dagum distribution are also provided. Finally, the novel generalized-Dagum model is illustrated by analyzing two real-life data sets related to the financial sector. The first data set represents the Bitcoin exchange rates vs the United States dollars. Whereas, the second data set represents the Ethereum exchange rates vs the United States dollars. Using the Bitcoin and Ethereum exchange rates data sets, the fitting power of the novel generalized-Dagum model is compared with the transmuted Dagum distribution and a new modified Dagum distribution.

Ідентифікація матриці суміжності у моделі імпульсних процесів з різнотемповою дискретизацією в когнітивній карті застосування криптовалют

Застосування криптовалюти на фінансових ринках характеризується складною динамікою, яка відрізняється нестаціонарністю процесів і невизначеністю ситуації. На процеси застосування криптовалюти діють різні збурення, направлені на зменшення рівня довіри до використання криптовалюти. Тому при операціях з криптовалютою виникають ризики втрати користувачів, що призводить до зниження ціни біткоїна, що пов’язано з хибними загальними одночасними сподіваннями багатьох користувачів, які створюються маніпулюваннями трейдерів на фінансових біржах; різкого обвалу курсу криптовалюти в результаті звичайних махінацій на біржах, до яких можна віднести так званий високочастотний трейдинг, який полягає в перевазі певної групи користувачів у швидкості купівлі грошових активів раніше за більшість інвесторів і продажу їх повільним користувачам, поки інформація про купівлю дійде до повільного інвестора. Ці дії в поєднанні з алгоритмічним трейдингом, механізмом деривативів і квартальних ф’ючерсів, реалізованих на біржах, створюють реальну небезпеку значної зміни курсу від доволі незначних збурень, пов’язаних з відсутністю гарантії на збереження капіталу, вкладеного в купівлю криптовалюти, який призводить до певної істерії користувачів у процесі торгів на біржах. Для опису впливу даних ризиків розглянуто когнітивну карту (КК) застосування криптовалюти на фінансовому ринку, на основі якої описано динамічну модель імпульсних процесів КК у вигляді систем різницевих рівнянь (рівняння Робертса) з різнотемповою дискретизацією. При цьому виконана декомпозиція вихідної теоретичної моделі імпульсних процесів КК з однотемповою дискретизацією на підсистеми з швидковимірюваними і повільновимірюваними координатами вершин КК. Для цього моделі підсистем представлені з різнотемповою дискретизацією координат і взаємопов’язані між собою. Розроблені алгоритми ідентифікації коефіцієнтів матриці суміжності імпульсних процесів КК для підсистем на основі рекурентного методу найменших квадратів відповідно у швидкозмінному і повільнозмінному масштабах часу. На основі цифрового моделювання виконані експеримен­тальні дослідження швидкодії і точності оцінювання вагових коефіцієнтів матриць суміжності в моделях імпульсних процесів підсистем КК.

Analysis of the Competitive Environment in the Electronics Market: the Global Crisis of the Semiconductor Industry and Global Development Trends

The relevance of the study lies in the large-scale and progressive impact of the shortage in the semiconductor market on the key industries of modern electronics production. Currently, the production of microchips shows the highest growth rates, the sphere is directly associated with the fourth industrial revolution, so leading manufacturers are actively seeking either to increase market share or to gain leadership altogether. As a result, competition is intensifying both between individual companies and between countries. The purpose of the study is to determine the degree of influence of the shortage of semiconductors on the final cost of goods based on microchips. The main task is to analyze the dependence of the cost of video cards on the cryptocurrency exchange rate, the growth in demand for the extraction of which is one of the factors that influenced the semiconductor crisis. The article analyzes the current state of the semiconductor industry market, describes the key players, manufactured products by type. The chronology of events that led to the shortage of microchips – the main basis for any modern electronics – has been built. The article describes the main affected industries from the crisis in the semiconductor market, analyzes the consequences in quantitative and qualitative terms. Conclusions are drawn about the global role of cryptocurrency mining in the progressive shortage of microchips. In the course of the study, general scientific methods were applied, such as analysis, synthesis, comparison, as well as special methods (statistical-economic and econometric analysis). A paired linear regression model is constructed to identify the relationship between the Bitcoin exchange rate and the cost of popular video card models. As a result of the study, the relationship between the cryptocurrency exchange rate and the price of video cards was determined, and the stock index of the semiconductor industry was analyzed.

Рынок криптовалюты: состояние, проблемы и перспективы

The first cryptocurrency appeared in 2009 and since then, both the number of active cryptocurrencies and the number of owners of crypto accounts have been steadily increasing. Despite the lack of a unified position on the issue of its legalization and the legality of its use for settlements and transactions on a par with “real” money, more and more users, both among individuals and legal entities, are inclined to the prospects of cryptocurrency investments, as evidenced by the investment policy of a number of large Wall Street companies in 2020–2021. Thus, the relevance of the chosen topic is due to the need to analyze the dynamics of the young cryptocurrency market, which is developing according to its own laws, which differ from the mechanisms of functioning of traditional markets. The development of the cryptocurrency market is influenced by factors such as the number of investors and the degree of development of blockchain technologies. The purpose of the study is to identify the factors that control the dynamics of the cryptocurrency exchange rate and assess their impact on the current state of the cryptocurrency market. To achieve this goal, such scientific and practical research tasks were formulated as identifying factors affecting the popularity of cryptocurrencies in the world; describing the advantages and disadvantages of using cryptocurrencies; conducting a retrospective analysis of the world experience in regulating the cryptocurrency market and analyzing the dynamics of the value of cryptocurrencies, using Bitcoin as the first and most popular cryptocurrency in the world. Particular attention was paid to the problem of identifying the factor that influenced the downward dynamics of the value of cryptocurrencies since mid-September 2021. When writing the article, such methods as analysis of statistical information, analysis of the dynamics of individual elements of the market, a forecast based on consumer judgments, the study of global strategy directions, and others were used. The result of the study was the disclosure of the influence of a new factor on the cryptocurrency market – energy.

Technical trading rules' profitability and dynamic risk premiums of cryptocurrency exchange rates

PurposeThe study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.Design/methodology/approachThe study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.FindingsThe findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.Practical implicationsThe empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.Originality/valueThe study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

On fitting and forecasting the log-returns of cryptocurrency exchange rates using a new logistic model and machine learning algorithms

<abstract><p>Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model.</p></abstract>

DIGITAL IMPERATIVES FOR THE DEVELOPMENT OF THE FINANCIAL MARKET OF THE NATIONAL AND WORLD VECTOR

Аbstract. Trends in the modern financial market are characterized by new challenges of the financial environment and the impact of digital transformation, which form abnormal areas of development. Defining trends, digital models, determinants and technologies of the financial market has become an urgent necessity. The object of the study is the trends in the Ukrainian financial market and global markets of financial derivatives, Forex, ОТС markets group under digital transformation. For this purpose, we have applied the methods of nonlinear dynamics, time series, technical and fundamental analysis, modeling, forecasting, market divergence, fluctuations and behaviorism. The study of the financial market has identified: the dominant trend in digital imperatives and digital technologies of the financial space; the cyclic and behavioristic development of the Ukrainian financial market; the growth in actualization of activities of Ukrainian financial institutions, which is associated with the alternative parabanking services; higher growth rates (3,3 units) of exchange transactions with precious metals, the dependence of the dynamics of currency on the impact of the global digital imperatives; the growth in OTC derivatives trades (the rate of growth of 1.1 units). Determinants of trends financial market are: financial and economic policy, digitalization of society, force majeure. The study of forecast patterns of changes in trends has enabled to determine the economic and mathematical model for calculating the index of forecasting the price of a digital financial asset and income from a transaction with cryptocurrency. A close correlation-regressive relationship (linear R2 = 0.6361, exponential R2 = 0.6948, power R2 = 0.7142) of the cryptocurrency exchange rate from the trading interest is determined (the elasticity coefficient is 2.7 units).
 Keywords: financial indicators, determinants, financial assets, derivatives, patterns, digital financial technologies, forecasting, Forex, OTC.
 JEL Classification G12, G13, G17
 Formulas: 3; fig.: 7; tabl.: 3; bibl.: 16.

Стейблкоины как имплементация теории частных денег Хайека

Objective: to search for the new ways and forms of evolution of the institution of private money based on one of the types of cryptocurrencies called stablecoins.Methods: the study uses the approaches of the historical school by D. North, evolutionary economics and the Austrian school of economics, as well as the new institutional economics.Results: the initial tasks of creating a bitcoin have led, as a result of the hype around its value, to a distortion of the original concept of forming “private money” and to the use of cryptocurrency in illegal operations. The development of a private monetary paradigm is also hindered by the high volatility and unpredictability of the cryptocurrency exchange rate. The solution to this problem can be stablecoins, which are more capable of performing the function of “Hayek money”. The article considers the main provisions of the concept of de-monopolization of monetary circulation in Hayek’s theory, as well as the possibilities of its implementation, taking into account the institutional technological innovations that have occurred. The scheme of operation and distribution of stablecoins is studied on the example of the Tether cryptocurrency. The main reasons for the use of stablecoins as a private form of money, as well as some obstacles to their development, are identified. The possibilities of their further implementation in the real sector based on the system of flexible-collateral system of new issued tokens are considered.Scientific novelty: it consists in a holistic assessment of the evolution of the private money institutional structure based on the analysis of the characteristics and ways of implementing certain types of cryptocurrencies in the real sector. The prospects for the formation of the private money market based on the Hayek model are determined.Practical significance: it consists in the further development of the cryptocurrency market and the formation of the institutional environment of the private money market.

Financial instrument of the new economy or a pyramid scheme: The issues of cryptocurrency security

Subject. This article discusses cryptocurrency as a new financial instrument in the global financial system. Objectives. The article aims to consider the current situation of cryptocurrencies and justify the advantages and risks of their use. Methods. For the study of cryptocurrency, Blockchain technology, and Bitcoin and the US dollar relationship, we used a comprehensive approach based on systems, functional and structural, and correlation analyses. Results. Based on the analysis of the trends in the cryptocurrency market formation, the problematic aspects of their legal regulation, the investment aspect, and the changes in the cryptocurrency exchange rate against the US dollar, the article identifies some shortcomings of this financial instrument from the point of view of supervisory bodies. Conclusions. Cryptocurrency really has significant prospects to become a valuable financial tool in the world economy, despite the rather critical problems of their regulation. Cryptocurrency can be a substitute for the US dollar in international settlements due to its advantages in the form of transparency and decentralization, especially given the fact that in 2019–2020, the governments of a number of countries (EU, Russia) began to actively develop legislation to regulate this area.

Analysis of the Dependence of the Cryptocurrency Rate on Updates in the Technical Part of the Mining Process

Monetary policy bodies in almost all countries of the world, regardless of the level of development of the economy and financial market, were not ready for the emergence and rapid development of digital currencies and, in particular, for their virtual variety - cryptocurrency.The development of technology constantly brings something new into our life, sometimes completely incomprehensible and unpredictable. An example of such a phenomenon is cryptocurrency. Many studies aimed at finding the exact factors on which the rate of cryptocurrencies may depend have not shown significant results. This study is aimed at finding the dependence of the cryptocurrency exchange rate on updates in the technical aspect of the cryptocurrency mining process.

Convolution‐based filtering and forecasting: An application to WTI crude oil prices

AbstractWe introduce new methods of filtering and forecasting for the causal–noncausal convolution model. This model represents the dynamics of stationary processes with local explosions, such as spikes and bubbles, which characterize the time series of commodity prices, cryptocurrency exchange rates, and other financial and macroeconomic variables. The convolution model is a structural mixture of independent latent causal and noncausal component series. We propose an algorithm that recovers the latent components by evaluating the filtering density of one component, conditional on the observed past, present, and future values of the time series. Forecasts of the observed time series are obtained as a combination of filtered causal and noncausal component forecasts. The new filtering and forecasting methods are illustrated in a simulation study and compared with the results obtained from the mixed causal–noncausal autoregressive MAR model in application to WTI crude oil prices.