The work is devoted to identifying and tracking development trends, structural shifts in the economy under the influence of world markets, represented by non-stationary time series of gold, bitcoin and oil prices. The heuristic potential of the concept of the long and medium wave is used for forecasting purposes. The analysis of financial time series using the adaptive correlation coefficient is carried out. The dynamics of the traditional coefficient appears to be a significantly smoothed graph, which prevents sufficient qualitative analysis of the data. The results obtained are analysed to identify wave fluctuations, to determine the phases of growth, prosperity, recession and stagnation in the economy. An overview of the situation on the world markets for gold, bitcoin and oil based on the considered time series is presented. Based on the identified trends in the dynamics of these markets, short-term forecasting was carried out using ARIMA models and neural networks. The statistical calculations R environment is used.
COVID-19 pandemic has caused significant losses and an increase in the level of risk in the financial markets and global economy. Thus in this study, we model the crypto market volatility behavior during the COVID-19 crisis. GARCH (1, 1) and GJR-GARCH (1, 1) were applied to model the volatility clustering and leverage effects in the intraday day (15-minute interval) returns of Bitcoin, Ethereum, and Litcoin ranging from 11th April 2019 to 8th February 2021. The empirical findings from GARCH (1, 1) model indicates the presence of volatility clustering in the crypto market. Moreover, the results of the GJR-GARCH (1, 1) indicate the presence of leverage effects in the financial returns series of all three crypto currencies. Furthermore, the excess kurtosis confirms the existence of fat-tail phenomena in the crypto market. Overall, the findings from this study showed that in times of COVID 19 pandemic the crypto market returns series showed volatility persistence, fat-tail phenomena, and leverage effects. These outcomes provide a better understanding for financial investors to invest rationally and cautiously during pandemic times.
Today, cryptocurrencies and topics related to information technology are attracting more attention not only on the part of traders, but also scientists. More research is being carried out aimed at the thorough study of cryptocurrencies, as well as the search for ways to facilitate interaction with blockchain. The topic of data analysis for cryptocurrencies is becoming increasingly important as the number of companies dependent on cryptocurrencies is growing rapidly. There are problems related to the cryptocurrency trading process, such as forecasting prices and trends, forecasting volatility, building a portfolio, detecting fraud, analyzing indicators for various cryptocurrencies. To solve these problems, trading bots are used. Trading bots are software products or websites that offer so-called «algorithmic trading», as they automatically analyze the actions and indicators of the market, offer strategies to maximize the trader’s profits and increase his satisfaction. They can aggregate historical market data, calculate indicators, model the order fulfillment and can even be set up to execute strategies while the customer is asleep. When analyzing the needs of the market, it turned out that there was a lack of a chat bot that would help traders or simply persons interested in the topic of cryptocurrencies to receive fresh information about the latest changes in the market. The article considers the functions and examples of performance of the chat bot CryptoAlert, created by one of the authors, which helps users to always be aware of the latest changes in the cryptocurrency market. The main function of the bot is to receive notifications about significant changes in the price of the selected coin. The use of CryptoAlert facilitates the trader’s work and significantly increases the likelihood of successful trading in the market.
Research background: Since the financial crisis in 2008, numerous other cryptocurrencies have established themselves in the financial industry alongside Bitcoin. Although the validity of the user cases is still lacking, Bitcoin is already being used extensively in the institutional finance sector, among others. Here, the comparison of Bitcoin to other asset classes in mixed portfolio structures must be taken into account. According to the latter, far-reaching areas of investigation emerge by adding Bitcoin in the evaluation of risk-return ratios of mixed portfolio weightings. Purpose of the article: The objective of this paper is to examine, within the framework of Harry Markowitz’s efficiency theory, the impact of including Bitcoin as an investment asset for the risk-return ratios of mixed portfolio structures. Methods: The statistical analysis is based, among other things, on paired sample tests, where the return and volatility values are tested for significant differences in the selected test values. Findings & Value added: The statistical investigations show that the introduction of Bitcoin leads to advantageous return structures, but at the same time to significantly increased volatility values of the examined portfolio constellations. Setting a regional focus of the investment assets in the investigations led to a simplified evaluation basis and at the same time offers the scientific space for further investigations.
The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.
This paper examines the time-varying conditional correlations between Bitcoin future market and five FOREX future markets. A sixvariate dynamic conditional correlation (DCC) GARCH model is applied in order to capture potential contagion effects between the markets for the period 2017-2019. Empirical results reveal contagion during the under investigation period regarding the one sixvariate model, showing potential volatility transmission channels among the future markets. Findings have crucial implications for policymakers who provide regulations for the above derivative markets.
We highlight the considerable recent research that investigates how cryptocurrencies have been impacted by COVID-19. We highlight common threads of investigation and consider common findings and conclusions. We also provide suggestions for future research. Additionally, we provide an overview of a recent paper in which we report on a study that applies wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices,
This study considers a market-based economy that is composed of two asset classes: one is a digital, cryptocurrency, and the other is real, gold. We demonstrated that coins like (BTC, LTC, and DASH) can substitute a traditional safe haven “gold” in an intertemporal investment portfolio to become a new form of safe haven. The cryptocurrency follows a Jump-diffusion process. However, gold prices follow an Ornstein-Uhlenbek process to characterize the stochastic nature of the market. The stochastic optimal control approach, combined with the strategic asset allocation and the intertemporal utility theory, are used through the derivation of a Hamilton-Jacobi-Bellman (HJB) equation to determine an explicit solution of the optimal allocation problem for investors with CRRA utility function. We considered the Gamma Lévy process to solve the optimization problem. By using the secant method, we determined numerically the optimal percentage invested in the two asset classes at each time over the holding period. Our results showed that an investor can substitute gold by coins (BTC, LTC, DASH) from an investment portfolio perspective. Although Gold is supposed to be the traditional safe-haven asset, the digital currency seems to emerge as a new form of safe-haven value in a risky environment.
Research background: Bitcoin is defined as digital money in a peer-to-peer decentralized payment network, an amalgam hybrid between fiat and commodity currency without a real value. This digital currency is also independent of any government or currency administration. Purpose of the article: This article explores whether bitcoin works as a medium of exchange or relates to assets, focusing on its current use and future utility regarding its characteristics. Methods: Analysing bitcoin statistical features, we found no connection with traditional asset categories such as stock, bonds and commodities either in intermediate time, or periods of financial crises. Findings & Value added: The study suggests that investors’ abiding interest in bitcoins can have a positive impact on their liquidity in the real time.
This paper presents an empirical verification of the effectiveness and usefulness of investment diversification using the main stock exchange indices and Bitcoin. The objective is to determine the effects applying the Markowitz portfolio optimization theory, i.e., the advantages of applying the modern portfolio theory for institutional investors. The research offers an answer to the following question: what are the advantages and disadvantages of using Bitcoin in portfolio optimization? The paper contributes to the representation of the reach and limitations of the modern portfolio theory for institutional investors. The conclusion is that rational behaviour of institutional investors requires consideration of portfolio optimization using the Markowitz model, because it is possible to create portfolios which, on the basis of historical returns, provide desired returns alongside certain risks. The methodology includes the analysis of high frequency data, i.e., daily trading data were used. The results indicate that the use of the Markowitz portfolio selection method, with all its limitations, is desirable, possible and applicable, but that it entails serious flaws in the sense of neglecting transaction costs, foreign exchange differences and the real value in the stock market. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments both for the risk-averse investor as well as for those investors who have a greater appetite for risk. The conclusion is that rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model. However, given the high degree of volatility, investors should be very careful when making decisions about including Bitcoin in the portfolio.
A growing literature has employed the existing generalized spillover measures to measure the connectedness – or market integration – of cryptocurrencies. This method, while useful, does not properly control for the cross-correlations of the cryptocurrencies when computing aggregate spillovers from all others to any given cryptocurrency, whereas the joint spillover method of Lastrapes and Wiesen (2021) does. This paper further describes the novel multivariate conditioning sets employed by the joint spillover method. By employing these two techniques and evaluating the differences in the results, we demonstrate that controlling for the cross-correlations of cryptocurrencies matters for measuring aggregate spillovers and the overall connectedness of the cryptocurrency market. Using data on ten of the most traded cryptocurrencies, we find that the generalized spillover index overestimates overall connectedness by over nine percentage points relative to the new joint spillover index. This difference varies temporally and across cryptocurrencies.
Abstract This chapter aims to offer readers an entry point to the deep discussion of this volume and the rationale for the “Finance 4.0” system described in later chapters. What is money, why is it designed this way, and what could it become in the crypto age? The chapter contains three parts. The first part describes in rough strokes the basic functions of money and how today’s fiat money system implements them. The second part offers a modest critique of the fiat money system, arguing that many problems take root in the intimate power relationship between “money and state.” The final part presents two cases that address some of the shortcomings. The first is Bitcoin that infamously pursues a state-independent, decentralized conception of money. The second is Finance 4.0, a system that proposes a participatory multi-dimensional money system with built-in incentives for sustainable behavior. If more readers feel empowered to enter the public debate for a better money system in the twenty-first century, this short introduction achieved its aim.
Abstract In this article, we explore the application of blockchain, a type of distributed ledger technology (DLT), in the field of energy trading. Specifically, we focus on crude oil trade. We argue that the application of blockchain technology and supplementary smart contracts supports responsible sourcing in complex supply chains and helps reduce information asymmetry in both the physical trade and paper trade of energy commodities markets. In order to apply blockchain technology to the energy market, we begin with a discussion on the architecture of blockchain and the different types of blockchain that might be applied in this sector. Further to this, we examine the current oil trading markets, particularly their relevant components, as part of the discussion on blockchain application. Subsequently, we look at how the various types of blockchain may be applied to the markets and examine their advantages and disadvantages. In conclusion, we look at the legal issues that may arise from such application, the potential solutions, and the potential impact of blockchain technology on the United Nations Sustainable Developmental Goals in the future and how a green fintech application can be developed.
We look at the association between the price of a cryptocurrency and the secondary market prices of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units (GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to that cryptocurrency.
This paper aims to analyze the consequences of adding Bitcoin to an investment portfolio. The main methodology used is the Mean-Variance model combined with the Monte Carlo Simulation. Results show that Bitcoin can improve the Sharpe Ratio of an already diversified portfolio, however the inclusion of Bitcoin has to be done in proportions averaging 3.83 percent of the portfolio's weight. This paper also found that Bitcoin does not seem to behave as a safe haven/hedge asset during the Covid-19 pandemic.