The cryptocurrency market is represented by more than 6,099 different cryptocurrencies with a total market capitalization of USD 354,316 million with Bitcoin dominance over 60%. Despite the increasing amount of scientific research, a comprehensive analysis of factors influencing the price of cryptocurrency is still needed. Previous studies have focused on the Bitcoin capitalization changes, rather than relationships and dependencies between the price of different cryptocurrencies and other factors. The author proposed a multiple linear regression model, which can be used for the cryptocurrency price forecast. The author tested the hypothesis, that Bitcoin's closing price changes likely in response to changes in altcoin prices and Google search index as well. According to the conducted research, the price of Bitcoin depends significantly on Google's search index on the specific cryptocurrency name. The revealed multiple regression equation can be further used for creating operational analytical programs for forecasting the price movement of Bitcoin.
This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets.
The aim of this study is to determine whether successful predictions for cryptocurrencies such as Bitcoin can be obtained with different methods. The reason why Bitcoin prices (Bitcoin / $) are used in the study is that this cryptocurrency is still the most widely used cryptocurrency in the market, and the idea that it will successfully represent the overall state of the cryptocurrencies market. Financial market series may contain fluctuations for some reason, such as speculations. It also usually includes nonlinear changes. Such features lead to failures in obtaining forecasts for financial time series. In this study, with the GARCH model, one of the classicial time series models and LS -SVM method, a machine learning method, predictions of the Bitcoin price series were obtained, and model performances were compared. In the study, between January 01, 2017 and February 29, 2020, 1155 daily Bitcoin price series ( ) was used. In both models, the Bitcoin price series and the volatilities of this series were used, and external variables were not included in the models. For both models, forecasts were obtained for periods of 1 month, 2 months and 3 months. For GARCH and LS -SVM models, out of sample successful forecasting rates according to MAPE ratios were 98,0347% -95,3423% for 1 month; 97,9544% -96,1307% for 2 months and 98,1272% -91,4874% for 3 months, respectively. The GARCH model has provided more successful results for all three periods. The finding of the study is that the GARCH model can be used to obtain forecasts for the crypto price series.
Saiful Izzuan Hussain, Nadiah Ruza, Nurulkamal Masseran, Muhammad Aslam Mohd Safari
Dependence structure between financial assets plays an important role in risk management. This research investigates the dependence pattern between the stock market and the potential of cryptocurrency. We employed time- varying copula and Extreme Value Theory (EVT) to model the extreme dependence between the United States (US) index stock market (S&P500) and Bitcoin. Empirical results show risk diversification for holdings of the S&P500 and Bitcoin during extreme events seem to be effective. This paper contributes to a better understanding of the dependence structure of the financial market during extreme events. This information is useful for investors who are seeking for the cross-market diversification.
We examine the long- and short-run relationships between USD/EUR official rates and implicit exchange rates, through Bitcoin as a currency vehicle, over the period from March 07, 2016 to November 22, 2019. The results show that the two exchange rates are cointegrated and that the cointegrating vector is not statistically different from the theoretical one that results from the law of one price. In the short-run, the implied rate Granger-causes the official reference rate. Our main conclusion is that Bitcoin USD and EUR prices incorporate fundamental information from the USD/EUR official exchange rate
Prithviraj Lakkakula, David W. Bullock, William W. Wilson, Lakkakula, Prithviraj · 6 authors
A blockchain is a distributed, public or private digital ledger that uses cryptography to record transactions across computer networks to prevent records from being altered retrospectively. This paper illustrates the impact of blockchain technology on total cost, time, and risk in international commodity trading using a hypothetical case of soybean trade from Jamestown, North Dakota, to China. The results suggest that the savings include 2.3 cents per bushel of soybeans and a 41 percent reduction in the total time, including documentation and transit time. Further, the 5 percent value-at-risk model shows a reduction of 2.6 cents per bushel of soybeans traded using blockchain technology. These results are significant for agribusinesses and other agricultural stakeholders who are evaluating the benefit of adopting blockchain technology in international commodity trading.
The study examines the stability of Bitcoin price/returns volatility using an AR-GARCH model. The data for the study were the daily closing Bitcoin prices obtained from the bitcoin,com website for the study period 01/01/2013 - 31/12/2017.
The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.
The global economy can be regarded as a complex global adaptive system, which adapts and evolves according to the environment and the behavior of the agents existing on the market. A topic that is topical and can even affect the welfare of a country is the field of cryptocurrencies. Today, the most well-known phenomenon by people in this field is the emergence of bitcoin. Cryptocurrencies or virtual currencies are an emanation of the financial crisis that started in 2008, a crisis that had led to a decline in confidence of traditional bank. In this paper, we discussed the creation of possible speculative bubbles, we presented the virtual currencies, we applied techniques of multidimensional analysis of the data for their analysis, and we evaluated the effects that the appearance of the cryptocurrencies had on the cybernetic economic system in Romania. Also, the paper deals with a section on identifying risks in the field of cryptocurrencies. In the last part of this paper, we focus our attention on the viability of these coins and their future prospects.
Bitcoin, Ethereum and Ripple are the three major cryptocurrencies with market capitalisation exceeding 70% of all the cryptos. The covid-19 threats since the beginning of the 2020 are disturbing the financial markets all around the world. This study is an enquiry to model the fluctuations among the three cryptocurrencies with the hypothesis that whether a combination of two coins could explain the changes in the other coin. The study found that Bitcoin and Ripple are better explaining the Ethereum returns in the short-run. The negative error correction coefficient found in the regression indicates that if the daily log returns of ETH is above that of the daily log return of BTC by one point, the daily log return of ETH fall by 1.11 point. On the other hand, the positive error correction coefficient indicates that if the daily log return of ETH is above that of the daily log return of XRP by one point, the daily log return of ETH increases by 0.14 point.
The paper examines the existence of a causal relationship in Grangers' sense between the movement of Bitcoin prices and the price of gold at the global financial market, in order to answer the question whether it is possible to predict the movement of the Bitcoin price based on the movement of the price of gold in the world market, but also vice versa. The survey was conducted from January 1, 2019 to December 1, 2019. In the research was used the Granger causality test (1969). The research results show that historical data on the movement of gold prices in the world market cannot be used to predict the change in value and price movements of Bitcoin. On the other hand, the survey results indicate the possibility of a reliable application of the use of historical data on the movement of value and price of Bitcoin.
Nektarios Aslanidis, Aurelio F. Bariviera, Christos S. Savva
This paper adopts a versatile multivariate conditional correlation model to estimate daily seasonality in the returns, the volatility, and the correlations between stocks, bonds, gold and Bitcoin. Besides the well known seasonality in stocks and bonds, the day-of-the-week effect is also present in Bitcoin. Mondays are associated with higher Bitcoin returns, while Wednesdays with higher Bitcoin volatility. As opposed to previous literature, our results indicate strong evidence of Bitcoin’s leverage effect. Moreover, we show that daily correlations between Bitcoin and traditional assets are higher at the beginning of the week, while the volatility of these correlations decreases over the week. Our results offer interesting insights in terms of investment and portfolio diversification, that can be applied to the analysis of systematic risk asset allocation and hedging. Keywords: Day-of-the-week effect; dynamic conditional correlation; Bitcoin; volatility seasonality. JEL codes: G01; G10; G12; G22
We use portfolio sorting to examine cryptocurrency returns in connection to other asset classes. Using the 110 cryptocurrencies with the highest market capitalization from September 2014 to June 2021, we consider 23 financial and uncertainty factors. We find that cryptocurrencies have a strong relationship with measures of uncertainty, equity markets, foreign exchange, and precious metals. Our results provide evidence that cryptocurrencies are related to other assets through portfolio sorting, complementing studies that have found that factors related to the cryptocurrency market itself can explain their prices.
Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector auto-regression model with stochastic volatility. Using weekly data over the past 3 years, we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion. We argue that the two variables are related to the profitability and risk of trading across the exchanges, and the impulse response functions are consistent with our interpretation.
Finansal zaman serilerinde görülen değişen varyans sorununun (ARCH etkisi) sonucu olarak otoregresif koşullu değişen varyans modelleri bulunmuştur. Çalışmamızda, piyasa değeri en yüksek üç kripto paranın [Bitcoin (BTC), Ethereum (ETH) ve Ripple (XRP)] getirileri incelenmiş ve söz konusu getirilerde finansal zaman serilerine benzer şekilde ARCH etkisi bulunmuştur. Söz konusu üç kripto paranın volatiliteleri için en iyi modelin hesaplanmasında altı GARCH modelini karşılaştırılmıştır. Bu modeller sırasıyla GARCH (1,1), EGARCH (1,1), TGARCH (1,1), APARCH (1,1), CGARCH (1,1) ve ACGARCH (1,1) modellerinden oluşmaktadır. Çalışma kapsamında 01.10.2015 - 01.10.2018 tarihleri arasında Bitcoin (BTC), Ethereum (ETH) ve Ripple (XRP) kripto paralarının günlük kapanış verilerinden elde edilen getiriler kullanılmıştır. Volatilite tahminlerinde Bitcoin (BTC) ve Ethereum (ETH) için en iyi model EGARCH (1,1), Ripple (XRP) için ise APARCH (1,1) modeli bulunmuştur. Çalışma kapsamında bu modeller kullanılarak volatiliteler üzerinde negatif şokların pozitif şoklardan daha fazla etkisinin bulunduğunu gösteren kaldıraç etkisi incelenmiştir. Bitcoin (BTC) ve Ethereum (ETH) modellerinde kaldıraç etkisi bulunmamış, bununla birlikte pozitif şoklar negatif şoklara göre daha fazla volatiliteye neden olmuştur. Ancak, Ripple (XRP) volatilite modelinde kaldıraç etkisi belirlenmiştir.
Recently, cryptocurrencies have drawn considerable attention from investors around the world. Such digital assets have also raised numerous hot issues in academic fields. Among them, Bitcoin is the most well-known and most notorious. After it was created in 2009, Bitcoin kept rising in price and reached its peak in late 2017. After that, it plunged dramatically. Coincidentally, Bitcoin futures also launched in December 2017. We are curious about the role Bitcoin futures play in the Bitcoin market. In this study, we investigate the relationship between Bitcoin and Bitcoin futures. First, we compare the optimal hedge ratios using three different hedge strategies, the na?ve hedge, the ordinary least squares (OLS) method, and dynamic hedging with the bivariate BEKK-GJR-GARCH model. Dynamic hedging is the most effective of the three methods; the level of risk reduction is around 59%. Then we test whether the volatility of Bitcoin would be significantly different before and after Bitcoin futures (BTC) launched. Our results support the hypothesis.