Bu çalışmanın amacı, Bitcoin ile Euro/Dolar, İngiliz Sterlini/Dolar, Kanada Doları/Dolar, Japon Yeni/Dolar ve Çin Yuanı/Dolar gibi önemli döviz kurları arasındaki dinamik ilişkiyi incelemektir. Bu bağlamda, Bitcoin ve döviz kurları arasında ortalamada ve volatilitede yayılım etkisinin varlığını incelemek için Hong (2001) tarafından önerilen ortalamada ve varyansta nedensellik testi kullanılmıştır. Ayrıca, Bitcoin ve döviz kurları arasındaki kuyruk bağımlılığının varlığını araştırmak için Hong vd. (2009) tarafından önerilen risk durumlarında nedensellik testi kullanılmıştır. 19 Ağustos 2011 ile 6 Ağustos 2021 tarihleri arasında günlük verileri kullanarak, Euro, Pound ve Kanada Dolar’ından Bitcoin’e yönelik tek yönlü ortalamada nedensellik ilişkisi tespit edilmiştir. Öte yandan, varyansta nedensellik testi sonuçları, Bitcoin ile Euro ve Pound arasında çift yönlü bir oynaklık yayılım etkisinin olduğunu göstermektedir. Ayrıca, Yuan ve Kanada Dolar'ın Bitcoin'in varyansta Granger nedeni olduğu belirlenmiştir. Risk durumlarındaki nedensellik testi sonuçları, Euro ve Pound’dan Bitcoin’e yönelik nedensellik ilişkisine dair kanıt sunmaktadır. Bununla birlikte Bitcoin’deki beklenmedik kayıplar, Yen’deki beklenmedik kayıpların Granger nedenidir. Genel olarak, ampirik sonuçlar Çin para biriminin Bitcoin ile daha az entegre olduğunu göstermektedir.
This study uses a fractional integration method to evaluate the efficiency of cryptocurrencies before and after the period COVID-19 had been announced as being a pandemic. Evidence of long memory is confirmed across all subsamples. Additionally, we find a greater degree of persistence during the COVID-19 pandemic period than in the pre-pandemic period.
Andrii Bielinskyi, Oleksandr Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов · 6 authors
Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
Bu çalışmada ekonomik politika belirsizliğinin (EPU) kripto paralar üzerindeki etkisi panel veri yöntemleriyle araştırılmaktadır. Bu amaç doğrultusunda öncelikle küresel ekonomik politika belirsizliği endeksi ve en büyük dört kripto paranın aylık verileri elde edilmiştir. Çalışmada kullanılan kripto paralar; Bitcoin (BTC), Ethereum (ETH), BinanceCoin (BNB) ve Ripple (XRP)’dir. 2018:01-2020:12 dönemine ait verilerin kullanıldığı çalışmada, yatay kesit bağımlılığı ve homojenlik testleri gerçekleştirilmiştir. Daha sonra Kónya (2006) tarafından önerilen bootstrap panel nedensellik testi uygulanmıştır. Dört kripto paradan ilk sırada yer alan Bitcoin ile EPU arasında çift yönlü nedensellik ilişkisi bulunurken, son sırada bulunan XRP için herhangi bir nedensellik ilişkisine rastlanamamıştır. İkinci ve üçüncü sıradaki kripto paralarda ise EPU’dan bu paralara doğru tek yönlü nedensellik ilişkisi olduğu görülmüştür. Çalışmadan elde edilen bulgular, ekonomik politika belirsizliğinin kripto paraların değerleri üzerinde etkisi olabileceğini göstermektedir.
In this paper, we measure the risk interdependence of 12 major cryptocurrencies before and during the COVID-19 pandemic, based on a GARCH-Copula-VaR approach and a dynamic network analysis. We find that cryptocurrencies generally show high levels of volatility, speculation, homogeneity and tail risk contagion. Furthermore, the COVID-19 pandemic has a continuous impact on the cryptocurrency market. When financial institutions are increasingly investing in crypto assets, the hidden risks in the cryptocurrency market remain high. Therefore, this paper calls for attention on the cryptocurrency market from both investors and regulators.
Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.
Jarosław Kwapień, Marcin Wątorek, Stanisław Drożdż
Time series of price returns for 80 of the most liquid cryptocurrencies listed on Binance are investigated for the presence of detrended cross-correlations. A spectral analysis of the detrended correlation matrix and a topological analysis of the minimal spanning trees calculated based on this matrix are applied for different positions of a moving window. The cryptocurrencies become more strongly cross-correlated among themselves than they used to be before. The average cross-correlations increase with time on a specific time scale in a way that resembles the Epps effect amplification when going from past to present. The minimal spanning trees also change their topology and, for the short time scales, they become more centralized with increasing maximum node degrees, while for the long time scales they become more distributed, but also more correlated at the same time. Apart from the inter-market dependencies, the detrended cross-correlations between the cryptocurrency market and some traditional markets, like the stock markets, commodity markets, and Forex, are also analyzed. The cryptocurrency market shows higher levels of cross-correlations with the other markets during the same turbulent periods, in which it is strongly cross-correlated itself.
Purpose Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies. Design/methodology/approach This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions. Findings The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis. Research limitations/implications This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions. Practical implications The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times. Originality/value The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.
Meng Qin, Tong Wu, Ran Tao, Chi‐Wei Su · 5 authors
This paper clarifies the association between the Sino-U.S. bilateral relation (BR) and Bitcoin price (BCP) by applying the bootstrap full- and sub-sample Granger causality tests. It reveals that BR has positive and negative effects on BCP. The negative impact points out that Bitcoin is viewed as a tool to avoid uncertainties caused by the deterioration of BR, also proving that the strained relation between China and the U.S. can stimulate the Bitcoin market. However, this opinion is not held under a positive impact, the main explanation is that the burst of bubble weakens its ability to hedge risks. The above conclusion is not consistent with the theoretical model, underlining that the Bitcoin market is boosted by the deterioration of BR. Conversely, there is a negative influence from BCP to BR, meaning that the relationship between China and the U.S. can be reflected by the Bitcoin market. Under the complex and volatile international situation, investors can benefit from this investigation to compensate for the losses and keep their wealth. Also, it helps the related authorities to create a stable investment environment and promote friendly bilateral relations.
This paper examines information transmission between Bitcoin derivatives and spot exchanges using 15-minutes interval data over May 2016 - September 2020. We employ a novel econometric framework with Fourier approximation, taking structural shifts in causal linkages, on the prices, returns, and volatilities of BitMEX, the derivatives market, and five other major spot exchanges, Coinbase, Bitstamp, Kraken, CEX.io, and Poloniex. Overall, the results provide robust evidence of information flow between the derivatives and spot exchanges, implying the markets react to new information simultaneously. The results are of importance for investors conducting portfolio allocation exercises and risk management strategies.
Youcef Maouchi, Lanouar Charfeddine, Ghassen El Montasser
This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies' bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets' bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
This paper deciphers the correlation of volatility between Bitcoin, stock and gold, in the context of uncertainty. The wavelet analysis results indicate that the selected assets are primarily positively correlated with each other, specifically in periods when the economic policy uncertainty (EPU) is high. Furthermore, the logit regression confirms that the EPU and categorial EPU indices have heterogeneous effects on the interdependence between Bitcoin, the S&P 500 and gold. Therefore, our findings provide insights for policy-makers to reduce the adverse impact of uncertainty on financial asset volatility.
Mustafa Raza Rabbani, Amani Alshaikh, Ammar Jreisat, Abu Bashar · 5 authors
The authors make a fundamental initial effort to conduct a qualitative review on the digital financial revolution called 'Cryptocurrency’, mainly to provide a comprehensive discussion on whether Cryptocurrency is a threat to the Environmental, Social and Corporate Governance (ESG) investing goals? The study also aims to draw the current landscape and future landscape of Cryptocurrency in the global marketplace. The paper makes a qualitative review of the most recent and relevant articles on Cryptocurrency published using qualitative analysis. Key findings of the study reveals that Cryptocurrency is an exciting financial innovation, but it remains a financial experiment. Cryptocurrency fails the test of objectives of ESG investing. It is further concluded that the more and more production of Cryptocurrencies is not good for the environment, and it is a real threat to the ESG investment goals. The findings of the study will help the prospective and potential investors in better understanding of the Cryptocurrency as the long-term investment avenue in the global marketplace. Even though Cryptocurrency received overwhelming response in the last decade or so, the academic research on Cryptocurrency is still at the budding stage, primarily because the academic literature of financial technology is at the nascent stage. Therefore, the present study makes an honest attempt to fill this gap and provide a comprehensive analysis of Cryptocurrency in terms of ESG investment perspective.
Muhammad Kamran, Pakeezah Butt, Assim Ibrahim Abdel-Razzaq, Hadrian Geri Djajadikerta
Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.
Abstract Recent technological developments have led to economic changes that have an impact on the macroeconomic and microeconomic levels in developing countries, as well as in developed ones. The introduction of cryptocurrencies (Bitcoin is the first cryptocurrency, made public in 2009) into the economy through blockchain technology, generated a series of benefits, but also significant risks for citizens, companies and states. The main purpose of this article is to present the operating mechanism of the blockchain system and cryptocurrencies, their advantages and disadvantages and the attempts of the international authorities to regulate the crypto market. The authors will present also few case studies of tech start-ups that leveraged the versatility of blockchain principles into viable business propositions. Romania makes no exception in this field, so the authors will analyze and present the current status of this industry.
This paper used two frames based on the Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) model, namely the Dynamic Conditional Correlation (DCC) and the Baba, Engle, Kraft, and Kroner (BEKK) models. DCC parameters confirmed the significant results to assess the spillover effects for return volatilities of five cryptocurrencies (Bitcoin, Dogecoin, Ethereum, Monero, and Peercoin). It indicated that cryptocurrency market returns would be volatile, connected with the time-varying pattern. Most ARCH and GARCH effects were significant in estimating the three pairs of return-mining profitability, return-Tweet, and mining profitability-Tweet. For the cryptocurrency return and profitability pair, returns depended on future price returns and cross-volatility spillover and were greater than their own volatility spillover effect. Moreover, the BEKK diagonal model was found to be the best model for return-mining profitability. The research community can also gain valuable insights into cryptocurrency investment models, offering wider future areas of research.
Olufunke G. Darley, Abayomi Isiaka O. Yussuff, Adetokunbo A. Adenowo
Abstract This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.
Alexandra Mironeanu, Beatrice Irimia, Valentina Săndulescu, Casiana Teodoroiu
Abstract For the past years, cryptocurrencies have been a hot and controversial topic that has captured the attention of the whole tech world. Even if right now the portfolio of digital assets is considerable in size, the first cryptocurrency, Bitcoin, exploited its pioneer advantage and managed to remain at the center of attention both for the media and investors. During 2020, one of the most chaotic years, Bitcoin boomed in November 2020 almost doubling since the end of 2019. This boom is the result of a combination of factors, such as the fear of missing out translated into a chain reaction of public and private companies to consider Bitcoin a safe reserve asset, a hedging method against inflation, which represents a substitute for traditional hedging instruments, the infrastructure developed around it over the years, and lastly the hype created by influential figures through news and social media platforms. There have been many public figures that exhibited interest in cryptocurrencies through platforms such as Twitter, for instance Elon Musk, Bill Gates, Kanye West, Hugh Laurie, Mike Tyson and Gwyneth Paltrow are just some in a long list of celebrities that backed Bitcoin. This paper aims to analyze the impact that twitter posts have upon the evolution of Bitcoin, coupled with Tesla’s investment and recent statement of introducing Bitcoin as a method of payment in the near future. Our research tries to determine if the news and social media posts, such as tweets have an influence upon Bitcoin’s volatility and fluctuations.
Cryptoassets have experienced dramatic volatility in their prices, especially during the COVID-19 pandemic era. This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets (Bitcoin, Ethereum, and Dogecoin) as well as Gold. Multiple Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are analyzed. We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude, perhaps a manifestation of the fear of missing out (FOMO) effect. Our research is one of the first to use COVID-19-period volatility of financial assets (in-sample data) to forecast their later COVID-19-period volatility (out-of-sample data). This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data. Our results indicate that generally, the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data. We also evaluate the correlations among the assets using the Dynamic Conditional Correlation (DCC) framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years. The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased. Unsurprisingly, there is a strong positive correlation among the cryptocurrencies. Pairwise correlation among all four assets was stronger during the COVID-19 pandemic. Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.