Follow blockchain research across journals, conferences, and preprint repositories.
4,843 results · page 86 of 202
Negar Fazlollahi, Saeed Ebrahimijam
No abstract is available for this record.
Gregory Parker
No abstract is available for this record.
Almir Alihodžić
The main goal of this research is to evaluate the returns and risks of the following types of assets: Bitcoin, EUR Stoxx 50, gold, bonds: government bonds ICE Bof A 1-10 Year excluding Italy and Greece and the corporate bond index ICEB of A 1-10 Year AA. The paper tested a total of ten portfolios according to different scenarios for digital and financial assets. Also, in the paper, greater measures of risk and return were calculated with the aim of forming an optimal portfolio with minimal risk. The results of this research revealed that the correlation between Bitcoin and other forms of financial assets is generally low and negative, which can be a good instrument for portfolio diversification, and positively affect portfolio performance. Also, the results of this study showed that in terms of volatility and return measure of a total of ten portfolios, the second portfolio (whose structure consists of Bitcoin, Euro Stoxx 50, gold, government bonds ICE Bof A 1-10 Year - excluding Italy and Greece and the corporate index bond ICEBof A 1-10 Year AA) is the most optimal portfolio. The findings of this research can serve in risk and loss assessments of portfolio managers, investors, and regulators.
Mohammadhossein Lashkaripour
No abstract is available for this record.
Kassi Assamoi, Adelphe Ekponon, Zihan Guo
No abstract is available for this record.
Polina Ellina
No abstract is available for this record.
Mei Li
This research paper explores the relationship between the global economic policy uncertainty index (GEPU) and Ethereum price.By employing the Hodrick-Prescott Filter Decomposition, the price of Ethereum is decomposed into a trend component, which reflects the increasingly wide usage, and the cyclical component, which shows its character as a safe haven asset and a speculative financial asset.By examining the relationship between the GEPU and the cyclical component of Ethereum, I find that GEPU Granger causes cyclical Ethereum, and they have a cointegration relationship.Their error correction models also demonstrate that cyclical Ethereum responds in the short-run to changes in GEPU and deviations from long-run equilibrium.The dynamics make the cyclical Ethereum converge towards their long-run equilibrium relationship.
Zixuan Cheng
No abstract is available for this record.
Eric M. Kimani, Anthony Ngunyi, Joseph Mung’atu
Cryptocurrencies are considered to be among the most disruptive innovations done in the financial sector within the last decade. It is a digital asset that is designed to serve as a medium of exchange using cryptography. Financial modeling of cryptocurrencies is needed in order to determine the presence of dependence between currencies. Copulas functions assist in modeling dependency structure by making it possible to separate marginal distributions of a given multivariate distribution. The purpose of the study was to model dependencies of cryptocurrencies using copula Garch. The study proposed the use of copula Garch model to model the dependence of cryptocurrency price data. Bivariate copula was extended to Bivariate Copula Garch in order to model prices and measure the cryptocurrency dependence. Prices of the four cryptocurrencies (Bitcoin, Binance, Litecoin and Dogecoin) were analyzed to establish whether there exists any dependency. The results showed standard Garch (1,1) under the highly flexible ARMA-GARCH model was appropriate to identify the true patterns of index returns. Fitting the copula standard Garch (1,1) model to the currencies, it was observed that the pair Litecoin and Bitcoin has the highest tail dependence among the selected cryptocurrencies, which implies that change in prices of Litecoin will influence the prices of Bitcoin and vice versa is true. Optimization of the cryptocurrencies showed that Dogecoin has the best optimization. The results of this study indicate that investing on Dogecoin significantly reduces risk irrespective of significant correlation among Litecoin, Bitcoin and Binance. Standard Garch (1,1) is the best in identifying dependence between the cryptocurrencies.
Zhichuan Li, Imran Yousaf, Yasir Riaz
In this paper, we studied the extreme connectedness between Bitcoin and crypto-mining stocks using the quantile connectedness approach of Ando et al. (2022). We estimated the connectedness (i.e., the direction and strength of spillover effects) at the median, extreme lower, and extreme upper quantiles. Our results revealed a highly interconnected system, with Bitcoin identified as a net transmitter of shocks. RIOT and MARA also emerged as major net transmitters in the system, while GREE and NILE were net receivers. The spillover effects were more pronounced during extreme market conditions compared to normal conditions. Moreover, the connectedness of the system progressively increased, peaking in 2021 when China banned crypto-mining. The extreme and dynamic connectedness identified in this study offers valuable insights for investors regarding hedging strategies and portfolio allocation, as well as for regulators focused on financial stability and systemic risk.
Jyotsna Malla, C. N. M. Lavanya, J. Jayashree, J. Vijayashree
No abstract is available for this record.
Yosuke Kakinuma
No abstract is available for this record.
Erdinç Akyıldırım, Ahmet Faruk Aysan, Oğuzhan Çepni, Shaen Corbet
No abstract is available for this record.
Fernando Henrique Antunes de Araujo, Leonardo H.S. Fernandes, JOSÉ W. L. SILVA, Kleber E. S. Sobrinho · 5 authors
This paper has investigated the predictability of the top 10 cryptocurrencies’ price dynamics, ranked by their daily market capitalization and trade volume, via the information theory quantifiers. Our analysis considers the Complexity-entropy causality plane to study the temporal evolution of the price of these cryptocurrencies and their respective locations along this 2D map, bearing in mind after and during the Russia–Ukraine war. Moreover, we apply the permutation entropy and the Jensen–Shannon statistical complexity measure to rank these cryptocurrencies similarly to a complexity hierarchy. Our findings reflect that the Russian–Ukraine war affects the informational efficiency of cryptocurrency dynamics. Specifically, the cryptocurrencies notably showed a decrease in informational inefficiency (USD-coin, Binance-USD, BNB, Dogecoin, and XRP). At the same time, the cryptocurrencies with more expressiveness for the financial market, considering the volume traded and the capitalized market, were strongly impacted, presenting an increase in informational inefficiency (Tether, Cardano, Ethereum, and Bitcoin). It clarifies the potential of cryptocurrencies to mitigate exogenous shocks and their capability to use with portfolio selection, risk diversification and herding behavior.
Cal Abel
No abstract is available for this record.
Sean Grover
Exchange-traded funds (ETFs) investing in bitcoin futures contracts first listed for trading in the fall of 2021. This research evaluates the extent to which the returns of bitcoin futures and bitcoin correspond to determine if bitcoin futures provide an effective proxy for a direct bitcoin investment. A no-arbitrage framework for bitcoin futures is established, which provides the basis for the empirical analyses that follow. The empirical analyses of returns correspondence between bitcoin futures and bitcoin use daily and monthly returns to estimate single-factor asset pricing regressions, finding coefficients of expected magnitude and that bitcoin returns explain over 97% of the variation in bitcoin futures returns. This research also estimates two-factor asset pricing regressions that include a novel excess carry term. The two-factor regressions find statistically significant excess carry term coefficients and over 99% explained variation. Finding strong evidence that the returns of bitcoin futures and bitcoin closely correspond, this research concludes that bitcoin futures provide an effective proxy for a direct bitcoin investment.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.
Sinda Hadhri
No abstract is available for this record.
Kuo‐Shing Chen, J. Jimmy Yang
This study unveils the unique properties of crypto assets and investigates the dynamic connectedness between six prominent stablecoins and Bitcoin in comparison with Bitcoin/stablecoin pairs involving portfolio management. Empirically, using the DCC-GJR-GARCH and partial wavelet coherence approaches, we show that Bitcoin provides greater diversification potential benefits compared to stablecoins during the COVID-19 crisis. The evidence suggests that Bitcoin, as the dominant cryptoasset, can serve as a suitable asset for portfolio diversification against stablecoins. Besides, we evaluate potential hedging benefits of cryptocurrencies for market participants and find that stablecoins are poor hedging products in most of the cases considered. The optimal portfolio for the hedging strategy involving a mix of Bitcoin and stablecoins reveals that the weights assigned to stablecoins are lower than those for Bitcoin. In particular, our results provide timely implications for market participants whose crypto portfolios include stablecoins, especially after Terra’s collapse.
Sherin M. Omran, Wessam H. El-Behaidy, Aliaa A. A. Youssif
Bitcoin is the first and most famous cryptocurrency. It is a virtual currency that is operated in a decentralized form using cryptographic strategies called blockchains. Although it has experienced significant market acceptance by traders and investors in recent years, it also suffers from volatility and riskiness. Technical analysis is one of the most powerful tools used for trading signals’ allocation using some algorithmic strategies called technical indicators. In this research, a newly proposed multi-objectives decomposition-based particle swarm optimization algorithm is used to find the best parameter values for some technical indicators, which in turn generates the best trading signals for Bitcoin trading. In this context, three conflicting objectives have been used, i.e., the return on investment, the Sortino-ratio, and the number of trades. The proposed algorithm is compared to the original MOEA/D algorithm as well as the indicators using their original parameters. Results showed the superiority of the proposed algorithm during the training and testing periods over the other benchmarks.
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
In this paper, we introduce a new approach to multivariate forecasting cryptocurrency prices using a hybrid contextual model combining exponential smoothing (ES) and recurrent neural network (RNN). The model consists of two tracks: the context track and the main track. The context track provides additional information to the main track, extracted from representative series. This information as well as information extracted from exogenous variables is dynamically adjusted to the individual series forecasted by the main track. The RNN stacked architecture with hierarchical dilations, incorporating recently developed attentive dilated recurrent cells, allows the model to capture short and long-term dependencies across time series and dynamically weight input information. The model generates both point daily forecasts and predictive intervals for one-day, one-week and four-week horizons. We apply our model to forecast prices of 15 cryptocurrencies based on 17 input variables and compare its performance with that of comparative models, including both statistical and ML ones.
Mykola Pinchuk
This paper examines the response of major cryptocurrencies to macroeconomic news announcements (MNA). While other cryptocurrencies exhibit no reaction to major MNA, Bitcoin responds negatively to inflation surprise. Price of Bitcoin decreases by 24 bps in response to a 1 standard deviation inflationary surprise. This reaction is inconsistent with widely-held beliefs of practitioners that Bitcoin can hedge inflation. I do not find support for the hypothesis that the negative response of Bitcoin to inflation is due to its negative exposure to interest rates. Instead, I find support for the hypothesis that Bitcoin is strongly affected by the shift in consumption-savings decisions, driven by the rise in inflation. Consistent with this view, Bitcoin has negative exposure to a proxy for the consumption-savings ratio.
Pooja Lekhi
No abstract is available for this record.
Mohammadhossein Lashkaripour
No abstract is available for this record.