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4,843 results ¡ page 101 of 202
Ismail O. Fasanya, Oluwatomisin J. Oyewole, Johnson A. Oliyide
No abstract is available for this record.
Hongjun Zeng, Abdullahi D. Ahmed
Purpose This paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020. Design/methodology/approach The authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test. Findings (a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers. Originality/value For the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets.
ShuâHan Hsu
This paper examines and confirms the varying volatility of the relationship between cryptocurrency and currency markets at different time periods, such as when the market encountered multiple risk events including the USâChina trade war, COVID-19, and the RussianâUkraine war. We employ the Diagonal BEKK model and find that the co-volatility spillover effects between the returns of cryptocurrencies and currencies, with the exception of Tether and the U.S. dollar index, evolved significantly. Furthermore, the co-volatility spillover effects between cryptocurrencies and EUR have the largest effects and fluctuations. Large-cap cryptocurrencies (Bitcoin and Ethereum) have greater co-volatility spillover effects between them and currencies. Regarding the ability of cryptocurrencies to act as safe-haven for currencies, we observe that Bitcoin, Ethereum, and Tether served as safe-havens during the USâChina trade war, and Bitcoin was a safe-haven during COVID-19. During the 2022 RussianâUkraine war, Bitcoin and Tether were safe-havens. Interestingly, our findings point out that Bitcoin provides a more consistent safe-haven function for currency markets. Overall, by including multiple global risk events and a comprehensive dataset, the results support our conjecture (and earlier studies) indicating that the capabilities of cryptocurrency are time-varying and related to market status and risk events with different natures.
Kingstone Nyakurukwa, Yudhvir Seetharam
Abstract This study revisits stock market integration in Africa using an informationâtheoretic framework that quantifies the flow of information between exchanges. We use daily return data for seven MSCIâclassified African stock exchanges between 2011 and 2021. As Bitcoin has become an important asset class on the African continent, we also explore whether this cryptocurrency confers any diversification benefits. Our method holds that stock markets are integrated if there is a significant flow of information between exchanges. The results reveal a statistically insignificant flow of information among African stock exchanges, and for the few cases in which information flow is statistically significant, the magnitudes are low. South Africa is the most influential stock market, as it transmits most of the total transfer entropy (informational value) in the system. We also observe that African stock exchanges are weakly integrated with Bitcoin.
Thanakorn Nitithumbundit, Jennifer Chan
No abstract is available for this record.
Meriem Youssef, Bouthaina Ben Naoua, Fouad Ben Abdelaziz, Messaoud Chibane
Abstract This paper analyzes the diversification benefits of adding alternative cryptoâassets in a traditional portfolio, from the perspective of an investor who seeks to achieve multiple objectives. Our analysis is based on daily and weekly return data for eight different assets including Bitcoin, Ethereum, Ripple, for the cryptoâassets and NASDAQ, S&P500, DowâJones, CrudeâOil, and Gold, for traditional assets. We use both inâsample and outâofâsample estimation procedures to analyze these data sets. We apply the weighted sum of deviations goal programming method to solve a biâobjective optimization problem where investors optimize simultaneously portfolio risk and return. For a variety of investor characteristics, ranging from riskâseeking to riskâaverse, we show that augmenting portfolios with alternative cryptoâcurrencies improves portfolio performance and the efficiency frontier, compared to standard portfolios. This improvement is even more observable for riskâseeking investors, for both inâsample and outâofâsample estimation procedures.
Shimeng Shi
Abstract This paper presents an empirical analysis of bitcoin futures risk premia. Based on the relevant theories and empirical findings of commodity futures risk premia, we study a battery of predictors, including positionâbased measures, market microstructure factors, and macroeconomic variables. We find that trading activity and extreme sentiment of speculators and retailers present significant predicting power on the subsequent bitcoin futures price changes over different time horizons. We also find evidence that the lower transaction cost, the higher bitcoin futures risk premiums. Regarding macroeconomic variables, financial conditions index, TED spread, US M2 money stock, and funding cost of financial institutions could predict bitcoin futures returns. The return impact of net position changes of hedgers is likely to be affected by extremes in macroeconomic variables. Speculators behave like negative feedback traders, while retailers are positive feedback traders. This detailed analysis of the risk premia of this emerging derivatives market provides critical implications.
Yue Shang, Yu Wei, Yongfei Chen
No abstract is available for this record.
Hideaki Aoyama, Yoshi Fujiwara, Yoshimasa Hidaka, Yuichi Ikeda
Cryptoassets flow among players as recorded in the ledger of blockchain for all the transactions, comprising a network of players as nodes and flows as edges. The last decade, on the other hand, has witnessed repeating bubbles and crashes of the price of cryptoassets in exchange markets with fiat currencies and other cryptos. We study the relationship between these two important aspects of dynamics, one in the bubble/crash of price and the other in the daily network of crypto, by investigating Bitcoin and XRP. We focus on "regular players" who frequently appear on a weekly basis during a period of time including bubble/crash, and quantify each player's role with respect to outgoing and incoming flows by defining flow-weighted frequency. During the most significant period of one-year starting from the winter of 2017, we discovered the structure of three groups of players in the diagram of flow-weighted frequency, which is common to Bitcoin and XRP in spite of the different nature of the two cryptos. By examining the identity and business activity of some regular players in the case of Bitcoin, we can observe different roles of them, namely the players balancing surplus and deficit of cryptoassets (Bal-branch), those accumulating the cryptoassets (In-branch), and those reducing it (Out-branch). Using this information, we found that the regime switching among Bal-, In-, Out-branches was presumably brought about by the regular players who are not necessarily dominant and stable in the case of Bitcoin, while such players are simply absent in the case of XRP. We further discuss how one can understand the temporal transitions among the three branches.
Dimitrios Koutmos
No abstract is available for this record.
Cemal Zehir, Melike Zehir, Alex Borodin, Z. F. Mamedov ¡ 5 authors
Blockchain technology has emerging areas of deployment in diverse sectors and use cases. In this study, several potential application areas of blockchain with promising benefits have been identified in the natural gas industry. There is no single solution that can address different challenges and meet disparate requirements. Therefore, it is important to understand the needs of the natural gas industry and propose appropriate blockchain solutions. Moreover, in the literature, there is a lack of detailed case studies involving industrial experts from the natural gas sector. Expert opinion can be useful for prioritizing the most needed or expected blockchain application areas among several options. By considering privacy, authentication, speed, security, energy consumption, and costs, suitable blockchain types and consensus mechanisms can be determined. This study presents one of the first detailed case studies for tailored applications of blockchain in the natural gas industry. Through a two-staged semi-structured interview with executives from SOCAR Azerbaijan, the most important blockchain application areas and operational requirements were identified. Furthermore, the most suitable blockchain solutions that can address application-specific conditions and needs were determined. This study both, develops a replicable and reliable methodology to conduct detailed blockchain implementation case studies in the natural gas industry and various other sectors, and provides detailed insights into the primary application areas, operational expectationsârequirements, and implementation challenges specific to each application.
MĂźge SAÄLAM BEZGÄ°N, Selim GĂNGĂR
In this study, it was aimed to examine that the relationship between Bitcoin price, Bitcoin volume and bitcoin energy consumption, and to research leading indicators of Bitcoin and whether the stock markets of the 5 countries that produce the most Bitcoin act together. In this context, 2011-2022 monthly data of Bitcoin energy, Bitcoin price, Bitcoin volume, USA, China, Kazakhstan, Russia, and Canada indexes was regarded in the study. Diebold and Yilmaz (2012) spillover index and time varying parameter VAR (TVPVAR) methodologies were used in the study. In the result of Diebold and Yilmaz (2012) spillover methodology was observed that the spillover effect of the Bitcoin energy variable on the Bitcoin price is 3.5%. While was observed from leading indicators of Bitcoin to all series examined be spillover, the most spillover was observed that be from Bitcoin price to SP500 index. Net spillover index of Diebold and Yilmaz (2012) was calculated that is 4.54%. In addition to this, in the TVPVAR established model was examined the action and the reaction functions in 4, 8 and 12 months periods. In the action-reaction functions of the TVPVAR model, it was observed that the shocks at the 4, 8 and 12-month periods in Bitcoin energy prices spread with a similar intensity to the Bitcoin price. In the result of the study was observed that Bitcoin energy shocks spread to SP500, Shanghai, Kase and RTSI indexes in all periods, the shocks of the price and the volume shocks spread to these indexes in short periods.
Franklin Allen, Antonio FatĂĄs, Beatrice Weder di Mauro
We investigate whether the market for ICOs in 2017â2018 and 2021 showed signs of contagion from prices of Bitcoin and Ether. During phases of optimism, ICO daily returns display low correlations with those of Bitcoin or Ether. But when the bubble bursts, correlations jump to very high levels, signaling that the ICO market becomes a sideshow of the cryptocurrency dynamics. We demonstrate that this high correlation was not present during the Nasdaq bubble in the 1990s, signaling that the price dynamics of digital tokens seems to be driven by a common factor, much more than in previous bubbles.
Turhan Korkmaz, TuÄba Nur
Purpose: This paper aims to test the volatility models for Bitcoin (BTC) and the financial stress index (FSI) and examine the volatility spillover among them. This aim was reached by obtaining weekly data from the 7th of January 2011 and the 24th of December 2021. Methodology: First, volatility modelling for the series is provided, and GARCH (1,1) for the BTC series and IGARC (1,2) for the FSI series are determined as the most appropriate volatility models. Then, residual volatility series are created for each variable over the IGARCH (1,2) and GARCH (1,1) models for the volatility spread between the series. The volatility spread between the series is examined with the diagonal VECH GARCH method. It is concluded that there is a positive volatility spillover effect from the FSI variable to the BTC variable. Then, impulse-response analysis is performed on the volatility residual series created for each variable. The empirical findings from impulse response analysis support a risk transfer between BTC and FSI series. Results and Findings: Changes in the BTC return series and FSI series are caused mainly by themselves, and the series are most affected by their shocks. By comparing the variance decomposition of the volatility series with the analysis results, it can be said that the changes in the volatility series are caused mainly by each other.
Yu Yan, Yiming Lei, Yiming Wang
A monetary model is established to introduce that bitcoin does have the characteristics of a price rise when the economic situation is terrible under high risk aversion. At this time, Bitcoin has the property of a safe-haven asset, and when economic conditions are good and risk aversion is low, Bitcoin has a pro-cyclical nature. At this time, Bitcoin has a stronger property as a medium of exchange. To show the movement law of the bitcoin price, we make a logarithmic linearization of the model and simulation, and the result is consistent with the theoretical analysis. To better understand the role of bitcoin in the real economy, several standard portfolio models are used to measure the similarities and differences between gold and bitcoin in an investment portfolio. After the outbreak of COVID-19, bitcoin has shown stronger safe-haven asset properties.
Ratikant Bhaskar, Ahmed Imran Hunjra, Shashank Bansal, Dharen Kumar Pandey
No abstract is available for this record.
Mahdi Ghaemi Asl, Oluwasegun B. Adekoya, Muhammad Mahdi Rashidi
No abstract is available for this record.
Yi Li, Wei Zhang, Andrew Urquhart, Pengfei Wang
No abstract is available for this record.
Zhilin Yao, Jingdan Su, Jingwen Hu
In this paper, we build an autoregressive integrated moving average (ARIMA) model and analyze the short-term trends of the gold price (GP) and bitcoin price (BCP) based on historical data. In addition, we use MATLAB to perform statistics and analysis on the data, find that the probability of continuous depreciation or appreciation after day 5 decreases exponentially, and find the maximum and minimum fluctuations. Further, we build a trading strategy model that uses the Apriori algorithm to calculate the number of subsets where prices have risen or fallen for 5 consecutive days. Finally, we perform a sensitivity analysis on the established model.
Kexin Jin, Xichen Liu, Weize Zhang
As a popular cryptocurrency, Bitcoin has been an important investment tool in recent years. This study aims to analyze the factors that affect the Bitcoin price to help investors make better investment choices. Applying the simple linear regression model and Granger causality test to the data from January 2015 to December 2021, the research first examines the stationary of the data and then studies the relationships between Bitcoin price and other factors including Dow Jones Industrial Index, U.S. currency in circulation, U.S. disposable income. According to the result, all three factors have a positive effect on the price of Bitcoin and the Bitcoin price will in turn influence the Dow Jones Industrial Index and U.S. disposable income. This finding helps explain how certain economic indicators and Bitcoin prices interact. Since investment is always risky, investors must consider certain factors like the trend of DOW, M2, or PCI in advance to make a reasonable investment decision.
Shicheng He
In the changing circumstances and the conflict between Russia and Ukraine, International crude oil prices rose sharply in the short term. This study will review the existing literature on the reason for the fluctuation of international crude oil prices and the dynamic change of Bitcoin, Tether, and Ethereum. This paper will also empirically evaluate the impact of fluctuation of international crude oil prices on the yield of electronic cryptocurrency. This research finds that the increase of futures crude oil prices will have a positive impact on the yield of electronic cryptocurrency, but this impact is short-term. Additionally, the growth of crude oil prices will not lead to the increase in the daily volatility of electronic cryptocurrency.
Dongxue Han, Mingliang He, Longyu Wang
In the topic selection, we need to estimate the prices of bitcoin and gold according to the data given from 2001 to 2012. According to the estimated price, the initial amount is set as $1000, which is used as the principal for financial investment for a period of five years from 2016. In the whole modeling process, the main problems we need to solve are the following four points: the task 1 is the best investment strategy is given through the established model, and the investment value on October 9, 2021 is calculated. The task 2 is the best strategy of the model is proved. The task 3 is Determine the impact of transaction costs on transaction results. The last task is to Complete a memo with strategies, models, and results. In the whole modeling process, we first preprocess the data, which is arranged and classified in chronological order, and fill the data by interpolation fitting. LSMT algorithm is a neural network algorithm, which is suitable for the calculation of various long-term processes. The investment problem we study is a good application field. In the calculation process of the basic model, it is necessary to set the initial value, complete a series of processing, and process the hidden layer of LSTM unit. Take x as the output value, set the temporary hidden layer and new hidden layer, and verify that the size of the final output result is consistent with the label size. The hidden layer is transformed according to the sigmoid function proposed above. After calculating the hidden layer conversion, the error is back propagated, the input derivative of the file is obtained, and the overall error and record hidden layer are obtained. Then the calculated hidden layer difference is used to calculate the change of parameters and update parameters. Since bitcoin can be traded on any trading day, gold can only be traded on weekly trading days. For the convenience of calculation, we fix the transactions of bitcoin and gold as trading days every Friday. After receiving the benefits, the total assets of the cash flow as of the trading day are obtained by deducting the Commission to be paid. However, the model ignores the impact of bitcoin mining with different software and the fact that gold and bitcoin are not fixed on the same trading day, so there will be errors.
Md. Jamal Hossain, Mohd Tahir Ismail, Sadia Akter, Mohammad Raquibul Hossain
The popularity of Bitcoin increases with time and investors take it as an alternative investment due to continuous financial instability and uncertainty throughout the world.It can be an alternative not only for developed markets but also for emerging and frontier markets.Prior to now, researchers focused solely on developed markets.For this purpose, the present paper has explored the answer to the question of whether Bitcoin enables a hedge or diversifier or safe-haven against emerging and frontier stock market indices.Instead of previous analyses, here we have examined constancy relationships as well as time-varying relationships between Bitcoin with four stock indices of emerging and frontier stock markets of four different countries.We have applied the GJR-GARCH method to find the answer to the question, and we have also applied the Threshold Autoregressive (TAR) model for cross-validation of the findings.Our empirical results have shown that Bitcoin has safe-haven abilities are in normal and turmoil market situations for emerging and frontier stock markets.Also, we have found evidence of hedging and diversification properties.
Dimitrios Bakas, Georgios Magkonis, Eun Young Oh
This study aims to identify the main drivers of Bitcoin volatility. The empirical analysis is based on a dynamic Bayesian model averaging approach for twenty-two potential determinants. The results reveal that the most important factors for Bitcoin volatility are Google trends, total circulation of Bitcoins, US consumer confidence and the S&P500 index.