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4,843 results · page 171 of 202
Debojyoti Das, Corlise Liesl Le Roux, Rabin K. Jana, Anupam Dutta
No abstract is available for this record.
Tuotuo Qi, Tianmei Wang, Jianming Zhu, Ruyu Bai
The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, Bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the Blockchain Index is necessary. In addition, the volatility of bitcoin and the Blockchain Index is crucial for investors. Therefore, this paper uses the Granger causality test to explore the correlation between bitcoin and the Blockchain Index. Furthermore, their volatility is analyzed by a GARCH-class model.
Krishna Chakravarty, Manjusha Pandey, Siddharth Swarup Routaray
No abstract is available for this record.
Yüksel Akay Ünvan
In this study, the impacts of Bitcoin on Japan, China, Turkey and USA stock indexes were investigated. Nikkei225, SSE380, BIST100 and S&P500 were selected as the stock market indexes. The weekly data including dates between January 03, 2016 and December 16, 2018 were analyzed using EViews program and firstly the time-dependent, non-stationary data set was stabilized. Then, the stabilized data was analyzed with VAR(3) model according to Akaike information criteria. According to the Johansen cointegration test, 2nd model was selected as the most appropriate model for the study. The variables were rearranged with error correction model and then Granger causality analysis was performed. As a result of these analysis, it was determined that Bitcoin only affected BIST100 and that there were two-way causality relation between them. In addition, a one-way causality from Nikkei225 to SSE380, from SSE380 to Bitcoin, from S&P500 to Nikkei225 and from Nikkei225 to Bitcoin were observed.
Md Al Mamun, Gazi Salah Uddin, Muhammad Tahir Suleman, Sang Hoon Kang
No abstract is available for this record.
Leandro Maciel, Rosângela Ballini
Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool forcryptocurrencies market practitioners.
Muhammad Abubakr Naeem, Elie Bouri, Gideon Boako, David Roubaud
No abstract is available for this record.
Hao Dong, Liming Chen, Xinyi Zhang, Pierre Failler · 5 authors
In this paper, we measure the asymmetric volatility spillover among six virtual financial asset (VFA) markets from January 1, 2014, to September 30, 2017, using the volatility spillover index based on a Markov regime-switching vector autoregressive (VAR) model and conduct a static and dynamic analysis under different regimes. The static results show that asymmetric effects of total, internal and net volatility spillover, on average, exist in all six VFA markets under different regimes. The dynamic results show that total, directional, and net spillover have significantly asymmetric effects. Thus, the government should monitor the specific VFA regimes and improve market regulation.
Hayet Ben Haj Hamida, Francesco Scalera
In this paper we explores as to whether cryptocurrency returns exhibit asymmetric reverting patterns and we test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns. For these reason, we uses non-linear autoregressive and Markov-switching GARCH (SETAR-MSGARCH) models. We finds strong evidence of regime changes in the mean and GARCH process. In addition, we conclude that bad news and good news of the same size have same impacts for investors.
Hui-Pei Cheng, Kuang‐Chieh Yen
No abstract is available for this record.
Anwar Hasan Abdullah Othman, Adam Abdullah, Razali Haron
As crypto-currencies hold dual nature of a medium of exchange (currency) and an investment asset, some questions may arise about the potentiality of including crypto-currencies as liquid investment asset in financial institutions particularly in the banking sector to enhance their liquidity risk management and improve their portfolio diversification investment strategy. The objective of this study therefore is to examine the characteristics of Bitcoin currency based on the requirements of High-Quality Liquid Assets (HQLA) standards of Basel III and compare its volatility structure with other traditional asset classes that are already recommended by Basle III as HQLA. The study utilizes both descriptive and quantitative analysis using the GARCH family models to examine the volatility structures of these assets. The findings show that Bitcoin currency holds the same characteristics of HQLA, however; the risk of legality and recognition is still under consideration by legal authorities around the world and this risk will be eradicated in the future as crypto-currencies derive their legality from their real intrinsic value, multi-economic usefulness and not by law as in the case of fiat money currency. Furthermore, the symmetric volatility structure analysis shows the continuing persistence of volatility and predictability behavior in return series of Bitcoin currency and other- traditional asset classes in the U.S. market. However, Bitcoin’s stability has gradually improved over time. With regard to the asymmetric informative response, Bitcoin returns respond more to negative shock but it has no statistical significance, thus suggesting the lack of leveraging effect in Bitcoin market but this effect was found to be statistically persistent in other traditional asset class markets. In addition, Bitcoin returns show very low correlation with other traditional asset classes. All these imply that Bitcoin is a potential candidate as a hedge and asset diversifier, which is recommended to be included in the HQLA. This study provides some support to recent theoretical work on crypto asset return behaviour and liquidity risk management. The findings provide appropriate information about Bitcoin asset behaviour compared to other traditional asset classes which will enable them to make the right investment decision with regard to hedging, diversification and liquidity risk management. The findings of this study may assist in evaluating the suitability of including crypto assets into HQLA to improve the liquidity requirement standards and ensure that banks have an adequate amount of HQLA specifically during times of financial turmoil.
Xiao Fan Liu, Zeng-Xian Lin, Xiao-Pu Han
Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively exchanged cryptocurrencies. We aim to reveal and explain the homogeneity, i.e., the strong correlation of market performance, and the heterogeneity, i.e., the imbalance of popularities and sophistications, of the cryptocurrencies.
Aviral Kumar Tiwari, Adeolu O. Adewuyi, Claudiu Tiberiu Albulescu, Mark E. Wohar
No abstract is available for this record.
Chiao-Ting Chen, Lin-Kuan Chiang, Yi‐Cheng Huang, Szu-Hao Huang
Forecasting exchange rates is difficult because financial time-series data is too complicated to analyze. In traditional financial studies, economic models and statistic approaches were widely used for predicting exchange rates. Recently, machine learning and deep learning techniques have played increasingly important roles in financial technology studies. This study adopts a deep learning technique called relation networks (RNs) to predict the exchange rates of fiat currencies and cryptocurrencies. To discover the relationship among different currencies, the concept of visual question answering (VQA) is applied in RNs. We also propose a specially designed architecture for the feature extraction stage to consider both spatial and temporal relationships simultaneously. The experimental results show that the proposed approach can achieve higher prediction performance for cryptocurrencies with approximately 65% accuracy rate. We aim to improve traditional approaches and construct a model using the concept of VQA based on RNs to optimize the prediction performance between fiat currencies and cryptocurrencies.
Tomasz Tomczak
<ns3:p>Before 2008, one thing that could be said about the derivatives market was that they were anything but regulated. Nowadays, the same thing can be said about the current financial innovation: cryptocurrencies. However, there are many private law institutions which have not been regulated for centuries, yet they did not trigger a worldwide financial crisis. The reason for that is that the financial crisis constituted a complex phenomenon caused by many factors, occurring jointly and affecting the derivatives market in unison, not just by lack of regulation. This paper will elaborate whether factors which, in the opinion of the author, caused the recent financial crisis, currently exist, or can occur in the future, in the cryptocurrency market. Such a study will allow a conclusion to be drawn as to whether cryptocurrencies are capable of triggering a similar financial crisis as derivatives did before their current partial regulation.</ns3:p>
Varun Chauhan, Ginni Arora
Cryptocurrency is decentralized and electronic alternative of currency. Bitcoin is the best example of this type of currency, and after bitcoin hundreds of such currencies were launched in market. Cryptocurrency is more frequently used, as it is theft proof, accessible anywhere & anytime. By using cryptocurrency the settlement of money is instant. Portfolio management is a technique through which a person decides how to allocate the resources. It helps in making better decisions and in minimizing risks of loss. Cryptocurrency and portfolio management are mostly used in financial sectors like banking and stocks. This paper reviews about the use of cryptocurrency and portfolio management with its associated challenges.
Miriam Sosa, Edgar Ortíz, Alejandra Cabello
Abstract One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.
Ur Koumba, Calvin Mudzingiri, Jules Mba
This study is confined in analysing how the economic policy uncertainty (EPU) effects affect exchange rates on cryptocurrency assets in times of financial turbulence characterized by low confidence in the financial stock markets, and tranquil periods where the financial stock markets behave smoothly. Our research employs the D-Vine pair-copula method on daily selected cryptocurrency (Bitcoin, Ethereum and Ripple) prices within the period of the 10 August 2016 to the 23 February 2018. Our findings document the presence of the dependence between the US EPU and cryptocurrencies and indicate a significant correlation with Ethereum which exhibits a much better return.
M.K.K. Schwiede
No abstract is available for this record.
Mária Bohdalová, Michal Greguš
Nowadays Bitcoin as cryptocurrency takes a significant place on the global financial markets. This paper analyzes the Bitcoin closing prices and traded volume during the period from December 28, 2013 to January 22, 2019. This period is known as a period with rapid increasing of the Bitcoin closing prices, mainly in the second half of the year 2017. The aim of this paper is twofold. First, we compute the Hurst coefficient to discover the close price dynamics and traded volume using a fractal point of view. We have discovered an anti-persistent behavior in the traded volume and random character of bitcoin closing prices. Second, we propose an analysis of the relationship between the close prices and traded volume. Our findings show how changes in the high-price period differ from changes in the low-price period. We also found that high prices caused investors to be afraid to trade due to possible rapid decrease in bitcoin closing prices.
Mark Schaub, H. Banker Phares
This study examines changes in the value of Bitcoin after the BREXIT and US Presidential elections in 2016 to determine whether it and other cryptocurrencies experienced value changes associated with the vote outcomes. For comparative purposes, Bitcoin is also compared to currencies such as the Great Britain Pound, Mexican Peso and Special Drawing Rights to determine whether Bitcoin values change like currencies. Some evidence showed changes in the value of Bitcoin were weakly similar to store-of-wealth commodities like gold and silver. Overall, the election-window volatility of cryptocurrencies was much higher than the preceding one-year average for the BREXIT vote but less volatile than the one-year average for the US election vote.
Paraskevi Katsiampa, Κωνσταντίνος Μουτσιάνας, Andrew Urquhart
No abstract is available for this record.
Siwen Zhou
No abstract is available for this record.
Daniel Cerecedo Hernández, Carlos Armando Franco Ruiz, Mario Iván Contreras-Valdez, Jovan Axel Franco Ruiz
El objetivo de esta investigación es analizar la presencia de burbujas financieras o un comportamiento explosivo en cuatro criptomonedas: Ethereum, Ripple, Bitcoin Cash y EOS. La selección de los activos se basó en la capitalización de mercado. La metodología implementada fue una prueba simple y generalizada (SADF y GSADF) de una variación de la prueba aumentada de Dickey-Fuller propuesta por Phillips et al. (2011, 2015). Encontramos diez, siete, seis y siete comportamientos exuberantes en los activos mencionados, respectivamente. Esta metodología ha sido en gran parte inexplorada y podría emplearse de manera estándar en el sector financiero para cualquier otro activo. Esta es la primera investigación que detecta este tipo de comportamiento para un grupo de criptomonedas con frecuencia diaria. Con el presente trabajo y el artículo de Li et al. (2018), el 68,47% del mercado ha sido analizado bajo la metodología. En consecuencia, este comportamiento podría estar disperso en todo el sector.