In this paper, we investigate the link between the well-known traditional finance and economic asset class and the digital currencies. The present study is undertaken to investigate the impact of the COVID-19 on the Financial Markets and the four major Cryptocurrencies from January 2020 to May 2021 in Egypt and USA. On the process of investigating the impact of the COVID-19 on the financial markets the study assumes the COVID-19 cumulative cases, Death cases, and the Fatality ratio to be the independent variables, and the Stock returns for the two indices (EGX30 and S&P 500), and the Returns for the four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Tether) to be the dependent variables of the study. The study findings revealed that there is a negative relationship between the COVID-19 cumulative cases, and daily S&P 500 stock returns, and there is a negative relationship between COVID-19 cumulative cases, fatality ratio, and daily EGX30 stock returns. There is a positive relationship between COVID-19 world death cases and daily Bitcoin prices, daily Ethereum prices, and daily Litecoin prices. There is a negative relationship between COVID-19 world death cases and daily Tether prices because tether is the only important stable coin on the crypto market with significant market capitalization.
In this paper, we analyse the long memory process in the cryptocurrencies Bitcoin (BTC), Cardano (ADA), Binance Coin (BNB), Dogecoin (DOGE), Ethereum (ETH) and Ripple (XRP) from January 1st, 2018, to November 10th, 2022, which includes the 2020 and 2022 events. The results demonstrate that the daily returns are leptokurtic, and the distributions are non-Gaussian. We also observe non-linearity, implying autocorrelation or conditional heteroscedasticity in digital currencies. The DFA exponents reveal that throughout the Tranquil period, digital currencies with current values higher than 0.5 exhibited long memory in their returns. The BNB digital currency has an exponent of 0.5, indicating that the series were unpredictable throughout this period. As can be shown, all cryptocurrencies offer values of the DFA exponent greater than 0.5 in the Stress subperiod, implying that the higher the DFA exponent and closer to 1, the higher the persistence, as well as the autocorrelation between observations and stronger predictive ability. The findings support the evidence examined by the BDS test, namely that price movements are not i.i.d. (independent and identically distributed) and that investors have a high possibility of achieving above-average returns through arbitrage.
With the outbreak of the Russian-Ukrainian war, more and more Western countries have imposed sanctions on the assets of the Russian people overseas, which have triggered a crisis of confidence in the world's currency, the dollar.At this time, the popularity of Bitcoin has also resurfaced.People are starting to think about the future of Bitcoin.This paper starts from the basic technical means of bitcoin and discusses the possibility of the future development of bitcoin by analyzing the characteristics of bitcoin.We believe that although Bitcoin cannot become a world currency in the future, it can become an investment asset and a convenient means of payment.
This paper aims to find the connectedness between cryptocurrencies and traditional assets. Using daily data of three representative cryptocurrencies and three traditional assets over the period August 2015 to July 2021, this study explores the cross-sector connectedness between the cryptocurrencies market and the traditional assets market. The result shows that connectedness varies over time and External events (COVID-19, oil crisis) have a significant impact on connectedness. Furthermore, traditional assets are relatively independent of each other. Cryptocurrencies, as the main transmitter, can affect each other. During some COVID-19 pandemic, cryptocurrencies can give great shocks to the traditional assets market. The result sparks some new insights for investors and policymakers.
The aim of the paper is to evaluate the investment attractiveness of selected energy tokens from the point of view of the effectiveness measures applied to ordinary financial instruments. The authors also classify energy tokens among climate-aligned tokens and digital instruments of green investments financing. In this way, it was possible to compare energy tokens against traditional financial instruments. Furthermore, the authors attempted to investigate the relationship between the formation of returns of the researched energy tokens and the returns on stock and commodity markets. The results of the study indicate the low investment attractiveness of energy tokens compared to investments in stock markets, commodity markets and investments in major cryptocurrencies such as Bitcoin and Ethereum. The research therefore indicates that buyers of energy tokens today should not be driven by investment or speculative motives but rather by a desire to obtain a means of clearing energy trading, or other utility.
Purpose: The aim of the article is a comparative analysis of selected cryptocurrencies and gold in the context of the SARS-CoV-2 coronavirus pandemic. Design/methodology/approach: The study covered the stock exchange of Gold and the four largest cryptocurrencies in terms of market capitalization: Bitcoin, Ethereum, Binance Coin, and Cardano. The data for the analysis for the period 2020-2021 was taken from the internet platform www.coinmarketcap.com, where all cryptocurrencies are published in daily intervals, and from the Investing website www.investing.com for Gold. The analysis of data in the form of time series was carried out, based on the assumption that successive values in the data set represent successive measurements made at equal time intervals. Findings: Our findings prove that the studied cryptocurrencies proved to be resistant to economic fluctuations related to the pandemic crisis. Originality/value: We present original scientific research that provides useful information in a practical dimension for investors interested in the cryptocurrency market and safe assets, and anyone interested in the specificity of the problem at hand.
Renewable Energy Certificate (REC) is a market-based instrument and tracking mechanism for electricity generated from renewable sources as they flow into the power grid. The current REC issuance and tracking system is centralized, highly regulated, and operationally expensive. We proposed a blockchain-based, decentralized platform for REC issuance and trading by allowing greater traceability and transparency in transactions and reducing the operational costs of REC exchanges. The main design of the platform is to tokenize RECs and provides a decentralized, trustworthy mechanism for REC issuance, trading, verification, and retirement. The platform provides low costs, transparency, and easy to use. Representing RECs as blockchain tokens ensures that the trustworthy information is immutably recorded and available for all stakeholders to track and verify, thereby improving the reliability and security of the REC issuance and tracking systems. We present the design of the platform and detailed simulations of REC issuance and trading.
Jan 1, 2022·Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
Bitcoin's performance during the COVID-19 pandemic has drawn a lot of attention, with many researchers wondering whether bitcoin can act as a hedge against the stock market, and how exactly the COVID-19 pandemic has changed bitcoin's connection to the world. This paper aims to investigate the dynami
This article examines the impact of technological changes to cryptocurrency—known as “forking” that triggers blockchain splits—on market conditions. Despite the explicit distinction in log return distributions between the two splitting blockchains, adopting new technology does not result in a disparity in market conditions: no significant difference exists in market efficiency and long‐term market equilibrium between the two splitting blockchains. Technological changes accompanying market separation do not impede the underlying uniformity in market conditions. The findings suggest that mutual information flows linked to market liquidity explain the results between the new and old forks.
Jan 1, 2022·Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
As investment fever rises, investment strategy is a critical choice for investors. In this paper, based on the price data of gold and bitcoin from 9/11/2016 to 9/10/2021, the corresponding mathematical models are established by the LSTM, evaluation model, and single-objective optimization model in a
As the cryptocurrency market dynamically evolves, important financial and economic issues arise. The main focus of the present research is on the price of cryptocurrencies. Following the exploration of the literature base, special emphasis was put on the comparison between the crypto market and markets for different asset classes (gold, stocks, foreign currency) and on the identification of connection points. Next, the article focuses on the period after 2020, and applies the event study methodology in order to establish, how the two cryptocurrencies with the highest market capitalization (Bitcoin and Ethereum) reacted to selected events. These events mainly encompassed hacker attacks aimed at the systems that form the basis of the operation of cryptocurrencies, and also certain steps regarding their regulation and application. Overall, it was established that hacker attacks did not have a significant effect on the exchange rates of the two examined cryptocurrencies. Effects of regulatory action on prices are mixed, however even significant effects can be regarded as short-lived.
The emergency of cryptocurrency has caused a shift in the financial markets. Although it was created as a currency for exchange, cryptocurrency has been shown to be an asset, with investors seeking to profit from it rather than using it as a medium of exchange. Despite being a financial asset, cryptocurrency has distinct, stylised facts like any other asset. Studying these stylised facts allows the creation of better-suited models to assist investors in making better data-driven decisions. The data used in this thesis was of three leading cryptocurrencies: Bitcoin, Ethereum, and Dogecoin and the Johannesburg Stock Exchange (JSE) data as a guide for comparison. The sample period was from 18 September 2017 to 27 May 2021. The goal was to research the stylised facts of cryptocurrencies and then create models that capture these stylised facts. The study developed risk-quantifying models for cryptocurrencies. The main findings were that cryptocurrency exhibits stylised facts that are well-known in financial data. However, the magnitude and frequency of these stylised facts tend to differ. For example, cryptocurrency is more volatile than stock returns. The volatility also tends to be more persistent than in stocks. The study also finds that cryptocurrency has a reverse leverage effect as opposed to the normal one, where past negative returns increase volatility more than past positive returns. The study also developed a hybrid GARCH model using the extreme value theorem for quantifying cryptocurrency risk. The results showed that the GJR-GARCH with GDP innovations could be used as an alternative model to calculate the VaR. The volatile nature of cryptocurrency was also compared with that of the JSE while accounting for structural breaks and while not accounting for them. The results showed that the cryptocurrencies’ volatility patterns are similar but differ from those of the JSE. The cryptocurrency was also found to be an inefficient market. This finding means that some investors can take advantage of this inefficiency. The study also revealed that structural breaks affect volatility persistence. However, this persistence measure differs depending on the model used. Markov switching GARCH models were used to strengthen the structural break findings. The results showed that two-regime models outperform single-regime models. The VAR and DCC-GARCH models were also used to test the spillovers amongst the assets used. The results showed short-run spillovers from Bitcoin to Ethereum and long-run spillovers based on the DCC-GARCH. Lastly, factors affecting cryptocurrency adoption were discussed. The main reasons affecting mass adoption are the complexity that comes with the use of cryptocurrency and its high volatility. This study was critical as it gives investors an understanding of the nature and behaviour of cryptocurrency so that they know when and how to invest. It also helps policymakers and financial institutions decide how to treat or use cryptocurrency within the economy.
<abstract><p>The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.</p></abstract>
The great economic crisis has shown that the global financial system primarily protects those who are ,,too big to fail". In order to provide the common man at least a partial liberation from the hegemony of this bureaucratized and undemocratic system, Bitcoin was created, the first cryptocurrency that functions in a decentralized monetary system based on the blockchain. The emergence of cryptocurrencies, which are beyond the control of the traditional political and economic structures, has raised hopes that the world monetary system can be democratized and freed from the influence of inefficient regulatory institutions. This paper analyzes how realistic the scenario is that in the foreseeable future cryptocurrencies will prevail over traditional currencies, starting from the basic characteristics of cryptocurrencies, regulation of their accounting and tax status, mutual influence of monetary policy and cryptocurrency system, potential benefits that cryptocurrencies can offer to developing countries, as well as a summary of the advantages and disadvantages of cryptocurrencies and recommendations for their improvement.
Abstract The paper provides a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present robust econometric analysis of predictive regressions incorporating factors, which were suggested by Liu, Y., & Tsyvinski, A. (2018). Risks and returns of cryptocurrency. NBER working paper no. 24877 ; Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies , 34 (6), 2689–2727, as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, acceptance of Bitcoin, and Google trends measure of investors’ attention. Due to inherent heterogeneity and dependence properties of returns and other time series in financial and crypto markets, we provide the analysis of the predictive regressions using both heteroskedasticity and autocorrelation consistent (HAC) standard-errors and also the recently developed <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>t</m:mi> </m:math> t -statistic robust inference approaches, Ibragimov, R., & Müller, U. K. (2010). t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics , 28 , 453–468; Ibragimov, R., & Müller, U. K. (2016). Inference with few heterogeneous clusters. Review of Economics and Statistics , 98 , 83–96. We provide comparisons of robust predictive regression estimates between different cryptocurrencies and their corresponding risk and factor exposures. In general, the number of significant factors decreases as we use more robust t -tests, and the t -statistic robust inference approaches appear to perform better than the t -tests based on HAC standard errors in terms of pointing out interpretable economic conclusions. The results in this paper emphasize the importance of the use of robust inference approaches in the analysis of economic and financial data affected by the problems of heterogeneity and dependence.