To protect against risks arising from fluctuations in spot prices and better manage risk, investors might evaluate futures markets. The role of price discovery in the futures markets and the possibility of reducing certain risks increase the importance of researching the relationship between spot and futures prices. This study aims to determine whether there is a relationship between the Bitcoin spot prices and the Bitcoin futures prices. To this end, the relationship between the two markets is analyzed using Johansen Cointegration analysis and Vector Error Correction Model (VECM) using the daily data of the period 02.23.2017 – 08.31.2021. Unit root tests show that each series are not stationary at the level values and that the first differences of the series are stationary. The results of the cointegration analysis show that there is a long-term equilibrium relationship between the bitcoin spot market and the bitcoin futures market, and it is a single cointegration vector. The Granger causality test based on the vector error correction model was used to determine the causality relationship between the series. It has been determined that there is a unidirectional causality relationship from the Bitcoin spot market to the Bitcoin futures market. Bitcoin is a new financial tool that attracts the attention of investors. Investors make transactions on Bitcoin for speculative purposes. Therefore, unlike other investment instruments, spot prices in the bitcoin market affect futures prices.
This paper sets out to explore the nexus between economic policy uncertainty (EPU) and digital currencies. An integrated survey takes place based on eleven primary studies. Furthermore, an econometric analysis is conducted by the threshold ARCH, simple asymmetric ARCH and non-linear ARCH specifications covering the bull and the bear markets as well as the highly volatile period up to the present. Threshold ARCH is found to provide the best fit for estimations. Outcomes reveal that Bitcoin is strongly connected with EPU while Ethereum and Litecoin are not but are strongly linked with Bitcoin performance. Moreover, weak negative effects of the VIX on both cryptocurrencies are detected while oil exerts weak positive impacts on Ethereum. Overall, Ethereum and Litecoin could serve for diversifiers against Bitcoin or hedgers against traditional assets during highly stressed periods with the advantage of not being affected by economic policy uncertainty news.
Yash Wadalkar, Yellamraju V H Sai Tarun, Jaiesh Singhal, Reena Sonkusare
Bitcoin, one of the most famous and high-in- demand cryptocurrencies, is a type of digital asset that is extremely difficult to track and make predictions upon. In addition, Bitcoin price does not correlate with market- movements, therefore, predicting its price action and its locus is an ordeal. In this paper, we have followed a comparative analysis approach, wherein we are using four different models to predict the trend of BTC Time series data. The results justify that the models have achieved accurate forecasting trends. During the period of 16th to 31st December 2020, Bitcoin prices experienced considerably high swings, due to the increased demand for it. In quantitative terms, the prices experienced fluctuations to the tune of 8000 USD. Despite these enormous price changes, we were able to achieve a model, that helped us attain a Mean Absolute Error (MAE) of 153.55 USD and Mean Square Error (MSE) of 43231.80 USD. Conventional Bitcoin price predicting researches follow a single to two model approach. However, for a highly volatile asset like Bitcoin, making long-term predictions and generalizing them based on limited number of models results in low accuracy outputs. This gap has been bridged in our research, we have worked with different models, as well as fragmented the time intervals into smaller portions, post which the prediction was made for only 2 days. Using this approach, we attained results with least error rates. The results obtained clearly show that ARIMA is the best model for predicting the future trends for BTC time series data. It takes into account the different types of decompositions like Regular Trend, Sessional and Residual Trend making the model give the best results.
Abstract This paper adopts the fractional cointegrated vector autoregressive (FCVAR) model to examine high‐frequency price discovery of bitcoin spot and futures prices from December 18, 2017 to July 31, 2020. We find that bitcoin spot and futures prices exhibit long memory properties and they are fractionally cointegrated. The result shows that the bitcoin futures market dominates the price discovery process. Interestingly, during the Covid‐19 pandemic, the bitcoin price discovery leadership has switched to the spot market. Moreover, we find that the bitcoin futures market follows a long‐run contango. The nonfractional CVAR model overestimates the price discovery of the futures market.
Purpose: The purpose of this research is to analyze the price movements of bitcoin, which has become a new phenomenon in financial markets since 2009, the first year of its release, and can be defined as virtual money or crypto money, to be seen as a financial investment tool. Design/Methodology: In the study, volatility, return behavior and reliability as a financial investment tool are examined with autoregressive Conditional Variable Variance modeling. In this context, symmetrical and asymmetrical ARCH models were used. Findings: As a result of the analysis; it has been found that it has an asymmetric effect in the first period for the bitcoin return series examined with symmetric and asymmetric ARCH models. In addition, it has been determined that shocks occurring in the bitcoin return series according to the half-life criteria are exposed to the volatility effect for more than 30 days in each period. It has been determined that bitcoin, which is examined by periods, has higher volatility in its first years. Limitations: The volatility of bitcoin, which has become a new phenomenon in financial markets today, can be defined as virtual money or crypto money, has been analyzed. Originality/Value: In fact, there are many virtual currencies or cryptocurrencies traded in the market. However, among many virtual currencies, bitcoin is the most known and the most market volume. Analyzing the price movements of bitcoin, which has started to be seen as a financial investment tool, is of great importance in the framework of reliability. The examination made in this respect constitutes the original value of the research.
Amaç: Bu araştırmanın amacı, Bitcoin ve altcoin kripto para piyasalarında finansal balonların varlığını araştırmaktır. Tasarım/Yöntem: Çalışmada, Bitcoin ve piyasa değeri açısından Bitcoin’den sonra gelen ilk beş kripto para birimine (Ethereum, Litecoin, Chainlink, Ripple ve Cardano) ait veriler kullanılmıştır. Kripto para piyasasında finansal balonların tespitinde GSADF testi kullanılmıştır. Bulgular: Çalışma sonucunda Bitcoin ve altcoinlerde finansal balonlar tespit edilmiştir. Bitcoin, Ethereum, Ripple ve Chainlink için tespit edilen balonlar istatistiksel olarak anlamlı iken Litecoin ve Cardano için tespit edilen finansal balonlar istatistiksel olarak anlamlı değildir. Sınırlılıklar: Çalışmada altcoin kripto para piyasasını temsilen piyasa değeri bakımından ilk beş kripto para birimine ait veriler kullanılmıştır. 2021 yılı başında çeşitli borsalarda işlem gören dört binden fazla altcoin olduğu göz önünde bulundurulduğunda çalışmanın veri setinin çalışmanın kısıtını oluşturduğu söylenebilir. Özgünlük/Değer: Çalışmadan elde edilen bulgular, araştırmacılar, politika yapıcılar, profesyoneller ve yatırımcılar açısından önem arz etmektedir. Çalışmada kullanılan veri setinin güncel olması 2020 yılı sonunda gerçekleşen finansal balonların tespitini olanaklı kılmıştır. Bu nedenle de çalışmanın ilgili literatüre katkı sağlaması beklenmektedir.
The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.
Abstract This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.
Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou · 6 authors
Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users' financial loss in cyclic arbitrage from the price impact.
As blockchain and energy trading have become hot topics in industry and academia, this paper presents a brief literature regarding the blockchain-based energy trading in the fields of energy trading with blockchain. At first, the background and development process is presented, and then the applications of blockchain in energy trading are surveyed and analyzed. Finally, conclusions are summarized and important directions are highlighted in this field.
Cryptocurrency symbolizes of a new development in the financial sector since it is the world's first entirely decentralized digital payment system. The cryptocurrency known as virtual money is one of the most important innovations brought on by digitalization. The purpose of this study is to analyze the relationship between the cryptocurrency (Bitcoin, Monero, and Stellar) with macroeconomics variables known as stock price index (Dow Jones dan Nikkei), oil price (Brent Oil dan WTI), and exchange rates (Australian Dollar, Euro, and Pound Sterling). The data was obtained from investing.com on monthly basis for the period between January 2016 untuil December 2020. The analysis were conducted based on unit root test, co-integration and vector error correction model (VECM) in order to identify the relationship between the three selected cryptocurrencis with macroeconomic variables. The findings of this paper showed that there is cointegration between the variables. The Vector Error Correction Model (VECM) indicates that the Bitcoin model and Stellar model did not have a long-run relationship. While for the second model, Monero found to have a long-run relationship with the variables. This research contributes to the growing study on cryptocurrency while extend and complement the literature by sourcing the latest research paper on this related field.
Mahboob Ullah, Maria Shaikh, Imran Abbas Jadoon, Muhammad Azizullah Khan · 5 authors
Purpose of the Study: In this research, the association between the COVID-19 pandemic and cryptocurrencies' price volatility has been examined.
 Methodology: To check the contagion effects of the COVID-19 pandemic on the price volatility of cryptocurrencies: BITCOIN, LITECOIN, XRP(RIPPLE), and ETHEREUM, the prices of all four are deployed from 10th August 2016 to 10th August 2020. The exponential generalized autoregressive conditional heteroscedastic (EGARCH) model is used to check the leverage effect exists or not. Stata 16 has been used to execute all the tests.
 Main Findings: The study's findings indicated that the leverage effect on the price volatility is present for LITECOIN, XRP(RIPPLE), and ETHEREUM but not for BITCOIN.
 Applications of the study: This study is significant for investors to develop strategies for investments and secure the transactions and control the creation of additional currency units. Also, it gives insight to the policy and decision-makers to articulate proper guidelines to overcome or minimize the effect of COVID-19 on cryptocurrency.
 Novelty/Originality of this Study: The motive for taking the crypto market into account is that the crypto market is one of the emerging markets and has started to have significance worldwide, linking with financial markets and economic growth. The leverage effect of COVID-19 is considered in this study as the epidemic has affected the supply and demand of goods due to lockdowns, blockages, and disruptions in delivery chains that lead to undiminished economic growth.
The purpose of this paper is to select the best GARCH-type model for modelling the volatility of Bitcoin, Bitcoin Cash, Litecoin, Dogecoin and Ethereum. GARCH (1,1), IGARCH(1,1), EGARCH(1,1), TGARCH(1,1) and CGARCH(1,1) are used on the cryptocurrencies closing day return. We select the model with the highest Maximum Likelihood and run an OLS regression on the conditional volatility to measure the day-of-the-week effect. The findings show that EGARCH(1,1) model best suits Bitcoin, Litecoin, Dogecoin and Ethereum data and that the GARCH(1,1) model suits best Bitcoin data. The results show a significant presence of day-of-the-week effects on the conditional volatility of some days for Bitcoin, Bitcoin Cash and Ethereum. Wednesday has a significant negative effect on Bitcoin conditional volatility. Friday, Saturday and Sunday are found to be significant and positive on Bitcoin Cash conditional volatility. Finally, Saturday is found to be significant and positive on Ethereum conditional volatility.
Bitcoin, son yüzyılın en meydan okuyan girişim örneklerindendir. Bu doğrultuda Bitcoin’e olan ilgi de hem kripto borsalarda hem de akademik literatürde çokça yer almaktadır. Genelde yapılan çalışmalarda, Bitcoin fiyatı üzerinde etkili olabileceği düşünülen finansal varlık rasyoları dikkate alınmaktadır. Bu çalışmada ise gazete ve medya haberlerinde Para Politikası Belirsizliği (MPU) ile ilgili yer alan haberler dikkate alınarak oluşturulan endeksler kullanılmıştır.Bu çalışmada, Ağustos 2010-Ağustos 2020 dönemlerinde Bitcoin fiyatı ile ABD ve Japonya Para Politikası Belirsizliği (MPU) endeksleri arasında aylık veriler kullanılarak, Hatemi-J (2012) asimetrik nedensellik testi çalıştırılmıştır. Çalışmanın sonunda ABD ve Japonya para politikası belirsizliği ile Bitcoin fiyatları arasında ne tek yönlü ne de çift bir nedensellik ilişkisine rastlanmamıştır. Bu çalışmada yer alan veriler ve değişkenler göz önüne alındığında, ABD ve Japon para politikası belirsizliği ile ilgili haberler ile Bitcoin fiyatları arasında bir ilişki olmadığı sonucuna varılmıştır
Miguel Ángel Echarte Fernández, Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Ricardo Francisco Reier Forradellas
This article analyzes the monetary policy of major central banks during the economic crisis generated by the COVID-19 pandemic. Rising public debt in many countries is being financed through asset purchases by monetary authorities. Although these stimulus policies predate the pandemic, they have been significantly boosted as many governments face large financing needs. We have been in a low interest rate environment for years and some governments have issued debt securities at negative rates. In addition, the rise of decentralized cryptocurrencies, based on blockchain technology, has created greater competition in the international monetary system and many governments have considered the creation of centralized virtual currencies, known as central bank digital currencies (CBDCs). We will analyze some relevant cases, with an emphasis on the digital euro project. The methodology is based on the analysis of the evolution of monetary variables. Pearson’s correlation will be used to establish some relationships between them. There is a strong similarity in the expansionary monetary policies of central banks. Although the growth of the money supply has not been passed on to the CPI, it has been passed on to the financial markets and the price of assets such as Bitcoin or gold.
The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [ 1 ] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.
Seyram Pearl Kumah, David Adjei Abbam, Ransford Armah, Evelyn Appiah-Kubi
The COVID-19 pandemic provides the first widespread bear market conditions since the inception of cryptocurrencies. We test the haven properties of cryptocurrencies for African stocks and commodity markets in a pandemic implementing the frequency domain spillover index. Data spans 11th August 2015 to 28th August 2020 at a daily frequency. Findings show weak interconnectedness across markets suggesting non-contagion risk and that cryptocurrency are safe havens for African stocks and commodity indices from the medium-term. We find the major transmitters of spillover effects across markets to be time-varying and heterogeneous. This study provides significant risk diversification benefits for policymakers and investors in the African financial markets.
Bitcoin is a digital asset that was first mined in January 2009 after the global financial crisis of 2007–2008. Over a decade later, there is still no consensus across different market regulations on the classification, use cases, policies, and economic implications of bitcoin. However, there is an increasing demand for digital currency, as an alternative to fiat currency which would spur financial innovation and inclusion. This study reviews regulations on digital assets across countries. It further discusses some use cases for bitcoin to reduce financial risk and facilitate cross border transactions. The study also discusses challenges related to bitcoin such as: cryptocurrencies substitution, cross border financing, cyber risk and security, and benefits in terms of the effect of coronavirus on the speed of capital market innovation and hence bitcoin usage. The study concludes by examining the economic effect of bitcoin halving events on the U.S. capital market to better understand the influence of bitcoin on financial markets and key drivers of its intrinsic value. The empirical evidence from this study suggests that bitcoin halving events are associated with significant negative stock market reaction, signaling a trading tradeoff between cryptocurrencies and U.S. stock markets.
The objective of this study is to examine the movement of Bitcoin and the traditional currencies (USD, EURO, GBP and CNY) and the Bitcoin’s hedging of the traditional currencies. First, this paper observes the Bitcoin and four traditional currency exchange series: the USD, EURO, GBP and CNY. Second, it examines the fluctuation patterns of each series by using wavelet transform analysis, Third, a wavelet coherence analysis is applied to examine the interdependence between the Bitcoin and the four traditional currencies. The phase pattern analysis results indicate that the Bitcoin may not act as a hedging currency to replace the traditional currencies during the Covid-19 crisis. Another interesting result shows the rapid increasing number of the World Covid-19 Deaths (CovidDeaths) may not be the critical reason for the hyper price of the Bitcoin. The massive quantitative easing (QE) may be considered as the key reason for the soar-up of the Bitcoin price.
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.