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4,843 results · page 33 of 202
Aissa Djedaiet, Hassan Guenichi, Hicham Ayad
No abstract is available for this record.
Hassan Javed, Naveed Khan
No abstract is available for this record.
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Abstract Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.
Ijaz Younis, Anna Min Du, Himani Gupta, Waheed Ullah Shah
Decentralized Finance (DeFi) assets, commodities, and Islamic stock market cointegration are affected by technological innovations, market dynamics, investor behavior, and crises. This study investigates the dynamics of returns and volatility for three DeFi assets, six commodities, and three Islamic stock markets from December 2019, to March, 2023, and identifies higher spillover effects during crises. Links among the Cross-DeFi, commodity, and Islamic markets significantly influence returns and volatility during crises. Notably, the commodities index emerged as a pivotal and substantial transmitter of risk during the Russian-Ukraine war crisis, with Emerging Markets (EM) being a key recipient. However, during the COVID-19 pandemic, livestock indices assume the role of prominent risk-return spillover receivers. The findings indicate robust returns and volatility interconnected between DeFi assets and Islamic markets with a moderate level of connectivity among commodity groups. WDI, ACWI, and EM explained 75 % of the variance observed during crisis episodes. This study formulates strategic portfolio management within and between connectedness among return volatilities by highlighting the stability of DeFi assets, the diversification potential in commodities, and a balanced option in Islamic markets. Our study provides a deep and insightful understanding of the stakeholders across markets during crises. • Notable spillovers in DeFi, commodities, and Islamic markets during crises. • Commodities drove risk during the Russian-Ukraine war, affecting Emerging Markets. • DeFi stability, commodity diversification, and Islamic market balance guide crisis management.
Matthew Ambrosia, John Dorrell, Thomas Stockwell
No abstract is available for this record.
Abderraouf Mtiraoui, Hassan Obeid
No abstract is available for this record.
Melike Bildirici, Özgür Ömer Ersin, Yasemen Uçan
The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.
Simona‐Vasilica Oprea, Adela Bârã, Cristian Bucur, Bogdan-George Tudorică · 5 authors
This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. Previous investigations have explored the profitability of Bitcoin, yet our research delves into its relationship with energy costs. Regarding the trade-offs, the Pareto analysis reveals that trading profit and energy cost do not strongly inversely correlate. The range of outcomes shows a relatively uniform trading profit (from 1.302,85 to 1.310,22$), but a broader variation in energy costs (from 1.141,66 to 5.657,94$). While the trading profit remains stable, there is a wide array of options for minimizing energy cost, which is influenced by various constraints and market conditions. Solutions tend to cluster more in areas of higher energy costs. However, the variability in energy costs offers Bitcoin miners choices, allowing them to tailor strategies, whether that involves prioritizing energy efficiency, profit maximization or striking a balance.
Shiang Liu, Changyu Yang
No abstract is available for this record.
Hae Sun Jung, Jang Hyun Kim, Haein Lee
Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors.
Mohd Waseem, Shailendra Singh
Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology. This study is dedicated to predicting Bitcoin and Ethereum price trends from 2014 to 2024, covering the main period of growth and change in the cryptocurrency market. This research aims to develop a good forecasting model through a comprehensive analysis of historical price data, market trends, economic progress and macroeconomy. It uses techniques such as machine learning, time series analysis, and sentiment analysis to try to predict future price movements of Bitcoin more accurately and Ethereum. By revealing the fundamental principles influencing cryptocurrency prices, this research leads to a deeper understanding of the dynamics driving digital assets and their impact on the financial sector.
Xiangyi He, Yiwei Li, Houjian Li, Houjian Li
No abstract is available for this record.
Mustafa Koçoğlu, Xuan‐Hoa Nghiem, Ehsan Nikbakht
Purpose In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties. Design/methodology/approach This study employs the connectedness approach following the combination of Ando et al . (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method. Findings Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics. Practical implications In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks. Originality/value Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.
Fan Zhou
This study employs event study methodology to investigate the impact of various types of events on cryptocurrency market returns and volatility. The research focuses on six major cryptocurrencies—ETH, BTC, BNB, XRP, DOGE, and TRX—over the period from December 31, 2017, to October 30, 2023. Six types of events are analyzed: cybersecurity events, block reward adjustment events, political conflict events, public health emergency events, cryptocurrency recognition and support events, and social media sentiment events. The findings reveal that cybersecurity and block reward adjustment events have minimal and short-lived impacts on market returns. Political conflict events cause significant short-term return volatility depending on market expectations. Public health emergency events, such as the COVID-19 pandemic, have significant and lasting negative impacts on market returns. Cryptocurrency recognition and support events have significant and sustained positive impacts on market returns. Social media sentiment events have significant but short-lived impacts on market returns. The robustness of the results was validated through the analysis of abnormal returns during the event period. This study provides valuable insights for investors and policymakers in managing market volatility.
Piotr Fiszeder, Marta Małecka, Péter Molnár
Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin , Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail.
Xiangyu Zhang, Zhuming Chen, Shengyu Wang
No abstract is available for this record.
Emmanuel Joel Aikins Abakah, Nader Trabelsi, Aviral Kumar Tiwari, Samia Nasreen
Purpose This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management. Design/methodology/approach We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz’s connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management. Findings Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors. Practical implications There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies. Originality/value The study’s major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate.
Ritesh Patel, Sanjeev Kumar, Shalini Agnihotri
No abstract is available for this record.
Zein Alamah, Ali Fakih
No abstract is available for this record.
Sridhar Manohar
No abstract is available for this record.
Marija Iljinaitė, Nijolė Maknickienė
The financial markets are undergoing rapid transformations that raise fundamental questions about the effectiveness of traditional investment models and strategies. Nowadays, investment options are incomparably wider than ever before, and one of the areas of this global financial transformation is alternative investments, so the question is what might be the trends of one of these alternative investments, non-fungible tokens (NFT). The object of the study is alternative investments, such as NFTs. The article intends to reveal how NFTs might impact the valuation and trade of digital assets, as well as to identify the key advantages and risks associated with NFTs for investors and creators. The research will carry out cluster analysis of NFTs, which will help to better understand the NFT market, learn about possible prospects and developments, possible advantages and disadvantages, as well as the level of risk.
Kun Guo, Yuxin Kang, Qiang Ji, Dayong Zhang
Abstract Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.
Kristián Kalamen, Adrien Audoin, Rastislav Solej, František Pollák
The financial markets experienced a thrilling saga between 2020 and 2023, characterised by a series of unprecedented events and captivating dynamics that set the stage for a compelling exploration of the interaction between bitcoin prices and the S&P 500 Index. This study systematically examines the correlation between bitcoin prices and the S&P 500 Index using the Yahoo Finance dataset over a 48-month period. Using the extensive Yahoo Finance dataset and the analytical capabilities of R Statistics & R Studio, the present research covers a comprehensive period of 48 months (2020-2023). The study identifies a robust positive correlation, quantified by a correlation coefficient of 0.7726, indicating a significant alignment between bitcoin price movements and the S&P 500 index. Monthly price variables obtained from an open-source repository provide a comprehensive overview of the relative dynamics of these financial assets. This analysis provides valuable insights into the current behaviour of bitcoin and the S&P 500 index, as well as concise observations on the dynamics of their correlation.
Shiwam Sehgal, Jaspal Singh
This study employs the Maximal Overlap Discrete Wavelet Transform technique to analyze the wavelet-based correlations between Bitcoin, bond markets, and thirteen sectoral stock indices in India over the period from 2017 to 2023, focusing on the comparison of pre-and post-COVID-19 pandemic effects. The aim is to investigate the dynamic interrelationships and to understand the impact of the COVID-19 pandemic on these financial assets. The study period is divided into preCOVID-19 and post-COVID-19. Findings from the study reveal a minimal negative correlation between Bitcoin, bond markets, and the sectoral stock indices in the pre-COVID era, indicating a lack of significant interdependence among these assets. However, the scenario changes markedly in the post-COVID period, shifting towards a positive correlation. This shift suggests that the COVID-19 pandemic has altered the relationship dynamics, leading to a more interconnected financial environment where movements in Bitcoin have begun to show a significant positive correlation with the movements in bond and sectoral stock indices in India. The study contributes to the existing literature by providing empirical evidence of how external shocks, such as the COVID-19 pandemic, can influence the correlation patterns among different financial assets. It highlights the importance of considering the changing dynamics in financial market correlations for investors, policymakers, and researchers in portfolio diversification, risk management, and financial stability analysis. Further, it underscores the role of alternative investments like Bitcoin in the evolving market landscape, particularly in response to global crises.