Follow blockchain research across journals, conferences, and preprint repositories.
2,964 results · page 17 of 124
Barbara Będowska-Sójka, Agata Kliber
No abstract is available for this record.
Kose John, Jingrui Li
No abstract is available for this record.
Barbara Čeryová, Peter Árendáš
No abstract is available for this record.
Seyed Alireza Athari, Derviş Kırıkkaleli, Chafic Saliba, Victoria Olushola Olanrewaju
In recent years, cryptocurrencies have emerged as a prime digital currency and an important asset, and the financial system is emerging as an important aspect while artificial intelligence (AI) has advanced expeditiously. Although AI and Bitcoin are among the most important topics in the world, empirical findings in this area are very limited. Thus, this study aims to explore co-movement between AI and Bitcoin price using quantile-based approaches from 2012 to 2024. Remarkably, the low-to-mid quantiles of AI (0.15–0.50) and the mid-to-high quantiles of BITCOIN (0.30–0.80) show a continuously positive and substantial effect from BITCOIN on AI. When AI is in its low-to-mid quantiles (0.15–0.60), it has a large and favorable impact on BITCOIN, particularly in the mid-to-upper quantiles (0.35–0.95). The results are robust by Moment Quantile Regression and Quantile-on-Quantile KRLS methods. Based on these findings policies are suggested.
Zhengyang Chen
This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency’s deep financial system integration with important inflation implications for monetary policy.
Andrew Hornback, Robert E. Whaley
Bitcoin has emerged as a promising addition to long-term investment portfolios due to its lack of correlation with traditional asset classes. Spot bitcoin exchange-traded funds (ETFs) provide a secure, familiar, and convenient way to invest in bitcoin. Since their launch on 11 January 2024, they have garnered more than $75 billion in new assets under management and their performance relative to bitcoin futures and futures-based bitcoin ETFs has been nothing short of extraordinary. This study examines the performance of spot bitcoin ETFs during their first year of trading. In doing so, it highlights the complexities and inconsistencies in US regulatory decision-making.
Ch. V. Raghavendran, K. Chandra Mouli, Manu Hajari, A. Anil Kumar Reddy · 6 authors
Predictive modeling has emerged as a key focus for cryptocurrency market asset valuation due to its complex nature and high market volatility. The research looks into Ethereum price forecasting with the methods of autoregressive integrated moving average (ARIMA) and Facebook Prophet model and long short‐term memory (LSTM) networks. These models operate on historical Ethereum prices and show their efficiency regarding temporal pattern recognition and prediction accuracy. The ARIMA model helps reveal trends as well as seasonal patterns and irregularities within Ethereum price fluctuations. The Facebook Prophet model serves as a forecasting tool because it automatically handles peculiarities present within cryptocurrency price data. Time series forecasting with LSTMs becomes an advanced technique used to detect intricate patterns along with sustained dependency relationships between data points. The systematic process of preparing data and constructing models and assessing results enables proper utilization of LSTMs for predicting time series data with accuracy. Ethereum price datasets are applied to train the models which undergo performance evaluation using MPE alongside MAPE and RMSE along with MAE to reveal strengths and weaknesses during Ethereum price predictions. The evaluation shows that ARIMA and Facebook Prophet together with LSTM demonstrate success in modeling Ethereum price fluctuations. This research explores the effectiveness of time series forecasting methods for cryptocurrency price prediction yielding vital knowledge about reliable tools for financial market trend modeling. Current research findings will provide knowledge to investors and risk management professionals making decisions within the volatile digital asset space.
Elie Bouri, Amin Sokhanvar, Harald Kinateder, Serhan Çiftçioğlu
• Reveals significant positive predictability in the stock market–cryptocurrency nexus. • U.S. tech and semiconductor stocks and Nvidia predict cryptocurrency returns and vice versa. • Mutual returns predictability is significant across several quantiles and lags. • It generally holds when controlling for the U.S. dollar index and treasury market. • A trading strategy based on the cross-quantilogram outperforms a benchmark strategy. This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock market–cryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies.
Tetsuya Takaishi
The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $Δ$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $Δ\rightarrow 0$, which are smaller than 1/2, indicating rough volatility. The relative error is found to be $1\%$ for the widely used five-minute realized volatility. Performing a multifractal analysis, we find the multifractality in the realized volatility time series, smaller than that of the price-return time series.
Zhang Xiao
Nowadays, financial markets are becoming more and more complex, and new portfolios need to be built to cope with them. This paper aims to build a Markowitz model for portfolio research based on new calibrations for nine different industries. Firstly, the weights and minimum variance combinations are calculated by using valid information such as mean, standard deviation, variance, and covariance. Second, this paper aims to maximize the return of the portfolio, diversify the investment risk of the selected portfolio, and finally determine the optimal portfolio. The portfolio can be adjusted to reduce risk or increase return by adjusting the percentage of Bitcoin. This paper further explores the portfolio using Bitcoin as a variable. This paper derives the volatility and return of the least risky portfolio to be 11.04% and -0.46%, respectively, when the portfolio is calibrated without Bitcoin, and the volatility and return of its Sharpe optimal portfolio are 14.61% and 7.11%, respectively. When the portfolio contains Bitcoin, the volatility and return of its risk-minimal portfolio are 9.45% and 0.6%, respectively, and the volatility and return of its Sharpe-optimal portfolio are 16.31% and 37.35%, respectively. Ultimately, it is concluded that Bitcoin has some risk-reducing and return-enhancing effects.
Zia Ur Rehman, Wing‐Keung Wong, Naveed Khan, Hassan Zada · 5 authors
In recent years, the issue of worldwide uncertainty has gained more attention in academic literature. Therefore, the current study examines how the United States (U.S.) economic policy uncertainty (EPU) affects various stock indices, commodities and cryptocurrencies. This study takes data on stock indices and commodities from February 2005 to December 2023 and data on cryptocurrency from October 2017 to December 2023. For estimations, we employ the Quantile-on-Quantile regression (QQR) approach to investigate the impact and to understand how changes in EPU affect stock indices, commodities, and cryptocurrency returns at different levels of quantiles. The findings reveal that EPU has a negative impact on the stock indices and cryptocurrencies. For stocks, high uncertainty leads to more volatility, while EPU exhibits higher volatility for cryptocurrencies, indicating sensitivity to policy changes. Similarly, commodities react differently to the U.S. EPU, while gold tends to appreciate in uncertain times. Furthermore, we employ quantile regression for robustness check, and the findings validate the outcome of QQR at various levels of quantiles from lower to higher. Moreover, the findings of this study are helpful for investors, portfolio managers, and policymakers to develop better investment strategies and effectively manage risks across different asset classes.
Kamphol Panyagometh
During the COVID-19 pandemic and subsequent periods of US monetary policy normalization after quantitative easing during COVID-19, global financial markets have encountered elevated levels of volatility and risk. In response, investors have increasingly sought out unconventional financial assets, such as Bitcoin, to mitigate exposure and enhance portfolio diversification. This study utilizes a Dynamic Conditional Correlation (DCC) Multivariate GARCH model, specifically employing the GARCH (1,1) specification, to analyze the relationship between stock markets index of major countries and cryptocurrency, with a particular focus on Bitcoin. The results indicate statistically significant correlations between Bitcoin and stock market returns in several countries during the COVID-19 period. Volatility appears to be influenced by historical stock market performance during both the pandemic and the subsequent normalization of monetary policy. Furthermore, the DCC-GARCH models reveal low significant coefficients for ASEAN stock market indices before and during the COVID-19 pandemic, indicating that these markets may have displaced Bitcoin as a hedge asset. In contrast, stock market indices in America and Europe consistently show statistical significance across all periods, suggesting that Bitcoin’s role as a hedge in these regions is limited. In contrast, gold clearly demonstrated safe haven properties before the COVID-19 pandemic which a characteristic had not been observed for Bitcoin. However, gold has emerged as a safe haven for only ASEAN stock markets since the U.S. initial 0.25% interest rate hike.
Ozan Kaymak
2008 yılında Bitcoin’in ortaya çıkmasından sonra kripto paralar kısa zamanda önemli bir varlık sınıfı haline gelmiştir. Kripto paralar; uzlaşma prensibine dayalı, birimler arası doğrudan işlem yapma imkânı sunan, işlemlere ait kayıtlara tüm birimlerin erişebildiği, merkeziyetsiz bir yapı olan blockchain teknolojisi ile işletilirler. Bu çalışmanın amacı, Forbes tarafından 2024 yılı için, blockchain endüstrisinde faaliyet gösteren firmalara ait sermaye varlıkları yatırımlarında uzmanlaşan en iyi borsa yatırım fonlarının 2021 Ekim ile 2024 Haziran dönemindeki haftalık getirileri ile aynı dönemdeki Bitcoin ve Ethereum haftalık getirilerinin zaman serileri Vektör Oto Regresyon Analizi ile incelenmesidir. Çalışmada Varyans Ayrıştırması ve Etki-Tepki Testleri yapılarak serilerin birbirlerine karşı etki düzeyleri incelenmiştir. Ayrıca seriler arasındaki nedensellik ilişkileri Granger Nedensellik Testi yöntemiyle araştırılmıştır. Çalışmanın sonucunda; seçili blockchain yatırım fonlarından First Trust SkyBridge Crypto Industry and Digital Economy (CRPT) haftalık getirilerinin, Bitcoin ve Ethereum haftalık getirileri ile %5 anlamlılık seviyesinde tek yönlü, sadece Bitcoin haftalık getirileri ile %10 anlamlılık düzeyinde çift yönlü Granger Nedensellik ilişkisine sahip olduğu belirlenmiştir.
Sahar Loukil, Noshaba Zulfiqar, Dimıtrios Paparas, Bikramaditya Ghosh
Climate change impact on the Blue-Green economy has been of great concern. Further cryptocurrency mining is impacting the economy in an adverse fashion. Moreover, impact of gold mining, extraction on Blue-Green economy and even relationship with cryptocurrency is another interesting facet. Therefore, we delved into the interconnectedness among five indices, two of which focus on the green economy (ICLN-iShares and CNRG-SandP), whereas three are on the blue economy (BJLE- BNP Paribas ESG Blue Economy ETF and PIO-Invesco Global Water ETF) and OCEN (IQ Clean Oceans ETF) alongside the traditional assets Bitcoin and gold indices. We considered between October 26, 2021, to January 5, 2024 for the study. This study highlighted some cardinal findings. First, BJLE can be used as a hedge against OCEN and PIO (all are in Blue economy). Second, excessive water usage in Bitcoin mining is detrimental to Blue-Green economy. Third, positive policy shock force spillover effect to cool down. Fourth, spillover typically increases as both economic uncertainty (US Banks collapse in 2023) and geopolitical risk (Russia-Ukraine conflict) increase. Fifth, there has been an increased responsiveness of these markets to immediate events (near-term bias). Therefore, this study would assist the policymakers and investors, especially in the Blue-Green domain.
Angham Ben Brayek, Hanen Ben Ameur, Farea Alharbi
The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or diversifiers in the portfolios of both OPEC and non-OPEC countries, focusing on their behavior during the COVID-19 pandemic. We employ a wavelet coherence approach to analyze the dynamic relationships between the variables. Portfolio optimization is conducted using CVaR to assess the effectiveness of these assets as safe havens, hedges, or diversification tools in mitigating financial risks during periods of heightened market volatility. The diversification benefits of commodities and Bitcoin in OPEC and non-OPEC stock portfolios decrease over time as their co-movement with stock markets increases. During the COVID-19 period, BTC did not act as a safe haven. However, gold served as a hedge for non-OPEC countries. Using CVaR, we found that BTC provides stronger diversification benefits than commodities, followed by gold. We examine the safe-haven role of Bitcoin and various commodities, specifically within the context of both OPEC and non-OPEC countries. Our study offers a more comprehensive analysis of how BTC and commodities function as portfolio assets during financial stress, providing valuable insights for investors and policymakers.
Johannes Schuderer
This paper examines the impact of Federal Reserve (Fed) monetary policy announcements on the cryptocurrency market, focusing on immediate market reactions. Using a sample of 57 monetary policy announcements from January 2018 to September 2024, the analysis distinguishes between expected and unexpected rate changes and isolates the unexpected component, constructing a measure of “surprise” rate changes with Federal funds futures data. The results indicate that unexpected policy changes exert a moderate negative effect on cryptocurrency returns, whereas expected changes have a small impact. The findings contribute to the literature by extending event-study methodologies to cryptocurrencies and, within the cryptocurrency literature, by focusing on the broad cryptocurrency market to offer a comprehensive perspective on market-level effects.
Diya Sharma, Renu Ghosh, Charu Shri, Divya Khatter
Purpose Cryptocurrency, an emerging asset class, is a virtual form of currency that uses cryptography for security and operates on decentralised networks based on blockchain technology. It offers both challenges and opportunities for investors, particularly in terms of diversification, risk management and potential returns. Considering this, the present study attempts to investigate the sentimental factors influencing cryptocurrency while unravelling the intricate interplay among these factors. Design/methodology/approach To achieve this, interpretive structure modelling (ISM) identifies the hierarchical model of critical sentimental factors, while Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) explores their dependency and driving power. Analytic hierarchy process (AHP) is adopted to rank the drivers. Findings Findings reveal that the pandemic, war, religiosity and economic uncertainty are top-level factors dominantly shaping cryptocurrency trends. Simultaneously, Google Search Trends and Herding emerge as the most dependent factors, influenced by sentiments that emerged from other factors. Practical implications The study unpacks implications, acknowledges limitations and proposes avenues for future research. Originality/value By exploring the interactive interrelationships among identified sentimental factors through ISM-MICMAC analysis and ranking via the AHP, this paper will have a great influence while contributing towards this evolving field.
OlaOluwa S. Yaya, Derick Quintino, Cristiane Ogino, Olanrewaju I. Shittu · 6 authors
No abstract is available for this record.
Haochen You, Baojing Liu
In recent decades, quantitative trading has been widely applied in both individual and institutional contexts through algorithms and automated trading. Price prediction and strategy decision-making are two crucial components of quantitative trading. While these two aspects have garnered extensive attention, the exploration and improvement of how to more effectively integrate them have been ongoing pursuits. In this paper, we construct a composite anti-risk trading strategy model based on short-term volatility identification and long-term trend prediction. The perfect combination of short-term fluctuations and long-term trends is achieved through the construction of parameters, such as Rpv, related to long-term trends. Simultaneously, the introduction of risk assessment indicators enhances the model’s ability to withstand risks. Integrating various modules, we obtain the SLRD model, mapping historical data to trading strategies. Utilizing real data from financial markets, we apply this model to cases involving gold and Bitcoin. The results show significant improvements when compared to previous models.
Xinyue Zhang
Gold and cryptocurrencies play an important role in portfolios, especially in risk management. Due to the special nature of these financial products, people usually add a small amount of gold or cryptocurrencies to the origin portfolio to balance return and risk. This article takes Bitcoin as the representative of cryptocurrencies to analyze the different impacts of Bitcoin and gold in the portfolio. This article employs copula functions to fit the Value-at-Risk, Conditional Value-at-Risk, mean return, and Sharpe ratio. Value-at-Risk and Conditional Value-at-Risk are used to measure the portfolio's risk. In addition, mean return and Sharpe ratio are used to measure the returns. Empirical results demonstrate that gold and Bitcoin can both serve as hedging assets; Bitcoin can enhance portfolio returns, while gold might lead to a decrease in portfolio returns. This result offers a reference on the asset allocation to investors. Adding an appropriate proportion of gold and Bitcoin can optimize the portfolio’s risk-return profile.
Dooyeon Cho, Kyung-Woo Lee
Abstract We construct a new daily measure of uncertainty about economic policy for Korea. The economic policy uncertainty (EPU) index is extracted from the reporting about economic policy in major Korean newspapers. We then investigate how daily EPU affects the Kimchi premium, which is the ratio of the Bitcoin price in Korea to that in the United States, adjusted for the exchange rate. Our findings indicate that an increase in Korea's EPU makes Bitcoin more expensive in Korea, while the U.S. dollar strengthens against the Korean won. The stronger appreciation of the U.S. dollar outweighs the increase in Bitcoin prices, thereby lowering the Kimchi premium. Similarly, an increase in U.S. EPU has comparable but weaker effects. The appreciation of the U.S. dollar almost entirely offsets the higher relative price of Bitcoin in Korea, resulting in no significant impact on the Kimchi premium from changes in U.S. EPU. In addition, the results suggest that the Kimchi premium tends to rise with increased trading volume in Korea but decreases as trading volume increases in the United States. We also document that while the Kimchi premium is positively associated with Bitcoin price volatility in Korea, it is not significantly related to that in the United States.
Zizhao Wang
Having emerged as a significant asset in the global financial landscape, particularly in the past decade, Bitcoin not only offers a decentralized alternative to financial traditions, but also potential for speculating and value storing. As Bitcoin matures, its impact on financial markets also grows rapidly, making it a critical subject for study. This paper focuses on the impact of seven selected key U.S. macroeconomic factors on Bitcoin prices by running a Vector Auto-Regression (VAR) model and performing Impulse Response Function (IRF) analyses. The indicators aim to stand for macroeconomic aspects including monetary policies, economic and market performance, inflation, commodity prices, and currency value. After obtaining quarterly time series from 2010 to 2024, a VAR model was utilized, attempting to capture dynamic relationships and lagged effects between the variables. The findings are expected to offer insights into Bitcoin’s dynamic interactions with macroeconomic conditions and prospects, especially for investors considering Bitcoin as a potential hedge in their portfolio and researcher interested in related topics.
Dhanraj Sharma, Ruchita Verma, Murad Baqis Hasan Al-Bukari, Mohammed A. K. Zaid · 5 authors
This study explores herding behavior in the cryptocurrency market during three major international crises: the COVID-19 pandemic, the Russia–Ukraine war, and the Palestine–Israel conflict. The study uses daily closing prices of five major cryptocurrencies (Bitcoin, Ethereum, Tether, BNB, and Solana) and the CRYPTO20 index data from December 31 2019 to May 20, 2024. The research employs the cross-sectional absolute deviation (CSAD) and cross-sectional standard deviation (CSSD) methods to identify herding behavior in the cryptocurrency market. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used for the robustness check. Stationarity of the data is verified using the Augmented Dickey-Fuller (ADF) test. The empirical findings reveal the anti-herding behavior in the cryptocurrency market during the three sub-periods. The study’s findings have important implications for investors, policymakers, and market regulators. Understanding the dynamics of herding behavior in the cryptocurrency market during global crises can help in developing strategies to mitigate the adverse effects of herding, such as inefficient asset pricing and increased market volatility.
Iulia Cristina Iuga, Raluca Andreea Nerişanu, Larisa-Loredana Dragolea
This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK model and the DCC GARCH model, the study spans data from February 17, 2020, to September 30, 2024, to analyze how cryptocurrencies, categorized by their environmental impact, influence these indices. The results reveal significant volatility spillovers from both clean and dirty cryptocurrencies, with clean cryptocurrencies such as Cardano showing a stabilizing effect, while dirty cryptocurrencies like Bitcoin exhibit more pronounced and asymmetric volatility impacts on green finance indices. Furthermore, the persistent correlations identified through the DCC GARCH model highlight the dynamic relationships between cryptocurrency markets and green finance, suggesting that shocks in cryptocurrency volatility can significantly affect the financial dynamics of renewable energy investments. These insights are valuable for portfolio diversification and risk management, indicating that certain cryptocurrencies may serve as effective hedging instruments against risks in green finance. This study contributes to a deeper understanding of the interaction between digital financial assets and sustainable investments, offering practical implications for investors, financial managers, and policymakers committed to achieving Sustainable Development Goals (SDGs).