Bu makalenin amacı kriptopara birimleri olarak da adlandırılan merkezi olmayan para birimleri olan Bitcoin Cash, Ethereum, Litecoin ve Ripple arasındaki ilişkilerin ortaya çıkarılmasıdır. Çalışmada üzerinde çalışılan dönem 03.08.2017 – 17.03.2020 tarihleri arasıdır. Çalışmada birim kök testi olarak Augmented Dickey-Fuller (ADF) testi uygulanarak serilerin durağan olduğu düzeyler saptanmış ve aralarındaki nedensellik ilişkisi Granger nedensellik testi ile sınanmıştır. Seriler arasındaki ilişkilerin yönü ve büyüklüğü, vektör otoregresif (VAR) model tekniğiyle belirlenmeye çalışılmıştır. Ayrıca, etki-tepki analizleri ve varyans ayrıştırma analizleri yapılarak serilerin standart sapmasında meydana gelen değişimin dönem bazında % kaçının diğer değişkenler tarafından açıklandığı ortaya konmuştur.
Amaç -Bu araştırmanın amacı, kripto para piyasasında en büyük kapitalizasyona ve en çok işlem hacmine sahip kripto para olan Bitcoin'in, volatilitesini en
Aleksandra Polyakova, Zavyalov Dmitry, Vladimir Kolmakov
We investigate the interrelation between the economic policy uncertainty index and composite cryptocurrency index to contribute to the contemporary discussion and verify the available results of other researchers. Our research objective is to veri
There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.
Two distinct and non-redundant understandings of volatility, as deviation from consistency, exist for a time-series: (1) exhibiting high standard deviation and, closer to the dictionary definition of the term, (2) appearing highly irregular and unpredictable. We find that Bitcoin is a prime example of an asset for which the two concepts of volatility diverge. We show that, historically, Bitcoin combines high Standard Deviation and low Approximate Entropy, relative to Gold and S&P 500. Moreover, subsample analysis for different time-scales (daily, weekly, monthly) shows that lower sampling frequencies drastically reduce the Kurtosis of the distribution of log-returns of Bitcoin. The opposite effect is observed for Gold and S&P 500. These properties suggest that, contrary to the volatility of the two traditional assets, Bitcoin’s high volatility is essentially an intra-day phenomenon that is strongly attenuated for a weekly or monthly time-preference.
This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.
While stablecoins such as Tether closely track the peg, there is some evidence for recurring spikes in stablecoins’ intraday volatilities rendering stablecoin volatilities unstable (Grobys et al., 2021). Using the Barndorff-Nielsen and Shephard (2006a) methodology, the purpose of our study is to examine whether jumps in Tether have an impact on (subsequent) Bitcoin returns. We retrieve hourly data for Bitcoin and Tether from Bitfinex covering the November 2018 to June 2021 period and encode the binary choice (1 – ‘jump’ and 0 – ‘no jump’) using bi-power variation based on asymptotic distribution theory at 5% significance level for each trading day. Our results show that the joint effect of positive jumps in Tether in association with an 1% increase in Tether returns on the prior day significantly predict negative prices changes in Bitcoin ranging from -3.65% to -8.49% in daily terms. Our results remain robust even after controlling for various other variables.
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.
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.
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.
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.
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.
I. S. Ivanchenko, Marina V. Charaeva, Alla A. Lysochenko, Ilya A. Nozhenkov
Since 2009, cryptocurrencies being a modern form of electronic means of payment have become widespread in the global financial market. In this regard, a study aimed to find an answer to the question: “Are cryptocurrencies a modern form of money?” was conducted. An analysis of the scientific works of leading economic schools has led to the conclusion that cryptocurrencies are a modern form of private money that performs the main monetary function being a means of payment, which corresponds to the idea of the Austrian economic school of full-fledged means of payment. The study attempts to predict the market rate of the three most popular cryptocurrencies at present being Bitcoin, Ethereum and Ripple due to the fact that modern cryptocurrencies demonstrate a high level of volatility in their market value, and reliable funds must maintain their purchasing power. The analysis of the cryptocurrency market with regard to the information efficiency has led to the conclusion that cryptocurrencies have been demonstrating instability of qualitative properties over the past five years. The authors proposed to improve the predictive characteristics of the HAR-RV model by additionally calculating the Shannon information entropy of the initial time series to level their insensitivity to unexpected information shocks in the cryptocurrency market being the main drawback of regression models. The study has proved that cryptocurrencies are a promising modern form of electronic money, their market rate is quite predictable, and the popularity of cryptocurrencies and their use in payment transactions will further increase.
In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is important to identify hedge or safe haven assets. Therefore, this paper analyzes changes in the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For this purpose, the M-GARCH model employed considers the interdependence among all the portfolio assets by using a time-varying asset pricing framework. This framework measures the impact of commodities and global stock prices as sources of systemic risk for Bitcoin returns before and after the COVID-19 pandemic. The evidence suggests that during the COVID-19 pandemic, the effects of changes in commodities and global prices on the idiosyncratic risk of Bitcoin were statistically significant. The idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted for by the proposed model rose from 86.06% to 95.05% during the pandemic. These results are in line with previous studies regarding the properties of Bitcoin as a hedge or safe haven asset for a portfolio composed of commodities and global stocks.