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4,843 results · page 23 of 202
Emiliano Ălvarez, Juan Gabriel Brida, Leonardo Moreno, AndrĂ©s Sosa
No abstract is available for this record.
Essa Al-Mansouri, Ahmet Faruk Aysan, Ruslan Nagayev
This paper examines Bitcoinâs viability as money through the lens of its risk profile, with a particular focus on its store of value function. We employ a suite of wavelet techniques, including Wavelet Transform (WT), Wavelet Transform Coherence (WTC), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC), to decompose the risk structure of Bitcoin and analyze its relationship with various systematic risk factors. Our dataset spans from 13 August 2015 to 29 June 2024, and includes Bitcoin, major commodities, global and US equities, Shariâah-compliant equities, Ethereum, and the Secured Overnight Financing Rate (SOFR). We find that Bitcoinâs risk profile is increasingly aligned with traditional financial assets, indicating growing market integration. While Bitcoin exhibits high volatility, a significant portion of this volatility can be attributed to systematic rather than idiosyncratic factors. This suggests that Bitcoinâs risk may be more diversifiable than previously thought. Our findings have important implications for monetary policy and financial regulation, challenging the notion that Bitcoinâs volatility precludes its use as money and suggesting that regulatory approaches should consider Bitcoinâs evolving risk characteristics and increasing integration with broader financial markets.
Fuzuli Aliyev, Neman Eylasov
No abstract is available for this record.
Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai · 5 authors
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the marketâs volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization.
Ximena Morales-Urrutia, Valeria Pillajo
This study delved into the complex world of cryptocurrencies, analyzing their behavior, profitability, and volatility. Through a thorough and meticulous analysis of the 2021 â 2023 period, the volatile nature of these digital assets was revealed, where profits could be suddenly affected by external events. Bitcoin, two of the cryptocurrencies with the largest presence in the market, were the subject of a thorough analysis using sound statistical methodologies. Descriptive statistics were employed to characterize the overall behavior of cryptocurrencies, including measures of central tendency, dispersion, and distribution. Additionally, normality and stationarity tests were used to choose the best variant of the GARCH model, which was EGARCH, to estimate conditional volatility, future volatility and price profitability, allowing to identify patterns and dynamics in their variability. The results of the study revealed that cryptocurrencies, while presenting attractive potential returns, also carry a high degree of volatility. However, thanks to the in-depth analysis of the behavior of these assets we can identify opportune moments to make purchases, sales or strategic investments. The main goal of this study is to provide investors with the information needed to make strategic and informed decisions about their cryptocurrency investment
Hind Alnafisah, Bashar Yaser Almansour, Wajih Elabed, Ahmed Jeribi
No abstract is available for this record.
Fahad Ali, Muhammad Usman Khurram, Ahmet Ćensoy
Abstract Investing in cryptocurrencies is progressively becoming a norm; however, these assets are excessively volatile and often decrease or increase in value instantly. Thus, rational investors holding cryptocurrencies for extended periods firmly search for assets that can diversify their risk, preferably with assets other than cryptocurrencies. In this study, we consider the two most studied cryptocurrencies with the highest capitalization and trading volume/value, namely Bitcoin and Ethereum. Specifically, we examine whether high-performing leading US tech stocks (Facebook, Amazon, Apple, Netflix, Google [FAANG]) can provide any diversification benefits to cryptocurrency investors. To do so, we employ dynamic conditional correlation (DCC), asymmetric DCC, time-varying parameter vector autoregression-based connectedness measures, dynamic correlation-based hedge and safe-haven regression analyses, portfolio optimization and hedging strategies, time- and frequency-based wavelet coherence, and high-frequency 10-min intraday data from January 1, 2018 to January 31, 2023. We find that FAANG stocks can be considered (at least weak) safe havens for Bitcoin and Ethereum during the sample period. Our subperiod analyses reveal that the safe-haven role of FAANG stocks, specifically for Bitcoin, has noticeably increased. While the safe-haven property of Facebook is the most promising, for Netflix it is blurred between a weakâsafe-haven and a hedge. Our findings may help investors, policymakers, and academicians to invest in cryptocurrencies, formulate relevant investment guidelines, and extend the literature on cryptocurrencies, respectively.
Serdar Neslihanoglu, Arzu Altın Yavuz, Muhammad Irfan, Alina Cristina NuĆŁÄ
Over the last decade, investors are interested in model fitting and predicting the future potential value of cryptocurrencies. For this purpose, the multiple linear, Ridge, Lasso and Elastic net regressions allowing for variable selection and regularization are compared. This comparison has yet to be undertaken in the literature. The analysis is implemented using weekly data (from 2015 to 2019) regarding Bitcoin (BTC) and Ethereum (ETH), especially with relation to Google and Wikipedia trends and 17 common factors, including stock market indices, gold and oil prices, central bank interest rates, exchange rates and policy uncertainty. The empirical findings favor the Elastic net approach, which outperforms the others in terms of model fit and predictability. Within the Elastic net framework, while the Google trend for the term "Bitcoin" (positively) has the greatest impact on Bitcoin price, the Chinese Yuan (CNY) to US Dollar (USD) exchange rate (negatively) has the greatest impact on Ethereum price. Based on study findings, essential policy implications are put forward.
Yue Qiu, Shen Qu, Zhentao Shi, Tian Xie
No abstract is available for this record.
Dimitrios Koemtzopoulos, ÎΔÏÏγία ÎÎżÏ ÏΜαÏÎ¶ÎŻÎŽÎżÏ , Nikolaos Sariannidis
(1) Background: Cryptocurrencies have a substantial environmental impact. In particular, the mining procedure that is employed to produce and finalize the transaction is energy-intensive and generates carbon emissions. Consequently, the objective of the present investigation is to investigate the function of cryptocurrencies in a sustainable development. This research specifically investigates the function of stablecoins, a novel subject in finance and academia that has the potential to foster a sustainable business environment. (2) Methods: A bibliometric analysis was performed using the R statistical programming language together with the bibliometric tools Biblioshiny and VOSviewer to fulfill the research objective. Data were obtained from the Scopus database, and their selection was completed using the PRISMA methodology. (3) Results: The results of the current research highlight the crucial role of stablecoins in promoting an alternative decentralized financial sector, offering a unique opportunity for the market to create a more inclusive and environmentally friendly financial ecosystem. Moreover, research indicates that stablecoins might convert Ethereum into a stable currency and enhance their ecologically friendly path. (4) Conclusions: Stablecoins have become a crucial tool in the unpredictable bitcoin environment, offering stability in a tumultuous market. The research indicates that users need to acknowledge the sustainability of asset collateral, and so far, only the regulation of stablecoins is progressing in this area.
Muhammad Shahzad Ijaz, Shoaib Ali, Anna Min Du, Mahrukh Khurram
We use event study methodology to examine how the Palestine-Israel Conflict affected equities, metals, energy, fiat, and crypto currencies. The findings highlight the susceptibility of the stock markets in Germany, the United Arab Emirates, Bahrain, and Kuwait to geopolitical shocks by demonstrating notable negative abnormal returns on the event day. This observation is more evident in areas which have direct economic connections to the belligerent nations. Conversely, the fiat and cryptocurrency markets, along with metals and oil, exhibit insignificant abnormal returns, with the exception of a strong reaction observed in Ethereum and oil prices. These findings highlight the fluctuating levels of sensitivity across diverse asset classes as markets beyond Palestine's trading partners demonstrate resilience to the war. Overall, our work underscores the significance of assessing contagion risk especially in areas affected by geopolitical instability. It also holds implications for policymakers and investors to contemplate the geopolitical situation while evaluating market risks and portfolio diversification strategies amid political tensions.
Khalid Khan, Adnan Khurshid, Javier CifuentesâFaura
Abstract This study uses the Bayesian structural model to assess the causal effect of the futures exchange (FTX) insolvency on cryptocurrencies from October 2022 to December 14, 2022. Findings show that FTX insolvency negatively impacts cryptocurrencies. Moreover, the results indicate rapid divergence from counterfactual predictions, and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse. Cryptocurrency is reacting strongly to the uncertainty caused by insolvency. In relative terms, the collapse of FTX has been highly detrimental to Solana and Ethereum. Furthermore, the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred. FTX collapsed owing to a mismatch between the assets and liabilities. The industry is still mostly unregulated, and regulators must act quickly, highlighting the need for outstanding innovation and decentralized and trustless technology adoption.
Samar S. Alharbi, Muhammad Naveed, Shoaib Ali, Faten Moussa
Using the TVP-VAR model, this study examines the connectedness between green cryptocurrencies and the individual components of the ESG (Environmental, Social, and Governance) stocks. Our sample period runs from November 10, 2017, to September 12, 2023. Our results indicate a moderate level of return and volatility transmission between green cryptocurrencies and ESG stocks. In line with theoretical argumentation, cryptocurrencies act as receivers of both return and volatility spillovers from the system, while stocks are the main transmitters. Our dynamic results show a substantial rise in total return and volatility connectedness of the system during the outset of the COVID-19 and Russia-Ukraine conflict, suggesting that global event amplifies the system connectedness. Moreover, the time-varying net results also exhibit a similar pattern, where the role of each asset changes during the turmoil period. Finally, our portfolio analysis suggests that green cryptocurrencies provide diversification to green stocks during both normal and turbulent periods. Additionally, they also emerge as effective hedges against ESG stocks across all market conditions. However, the hedge ratio increased during the COVID-19 pandemic, suggesting hedging becomes more expensive during turbulent periods. Our findings provide valuable insights for portfolio managers and policymakers regarding asset allocation, risk management, and the evolving dynamics between green cryptocurrencies and ESG stocks in an increasingly interconnected financial landscape.
Syeda Fizza Abbas, Aliza Sajjad, Haider Rizavi, Nadia Sadiq
Cryptocurrency, emerging post-recession, has the potential to reshape the financial landscape. Since Bitcoin's debut in 2009, cryptocurrencies have evolved into advanced assets using blockchain technology. These decentralized digital currencies stand out from traditional money by expanding banking access, cutting transaction costs, and enhancing security. Beyond technology, they shift trust and control in finance away from centralized entities like banks and governments, leveraging blockchain and distributed systems to boost efficiency and promote financial inclusion, especially in developing countries.
Emna Mnif, Nahed Zghidi, Anis Jarboui
Purpose Cryptocurrencies have transformed the financial landscape and raised environmental concerns, particularly distinguishing between energy-intensive (dirty) cryptocurrencies and environmentally friendly (green) cryptocurrencies. This study investigates the role of energy-intensive and ecologically friendly cryptocurrencies in sustainable investments, exploring their potential as hedging tools amid market and geopolitical stresses. Design/methodology/approach Employing a time-varying parameter vector auto-regression (TVP-VAR) connectedness approach, the research analyzes the interactions and spillover effects among clean and dirty cryptocurrencies, green bonds, and traditional financial assets. It also explores portfolio diversification strategies like minimum variance, correlation and connectedness portfolios, evaluating their risk minimization efficacy while incorporating green financial instruments. Empirical data on daily closing prices and financial indices are used to assess financial interconnectedness and evaluate portfolio diversification strategies. Findings Green bonds consistently provide strong hedging capabilities, while clean cryptocurrencies exhibit a more nuanced role influenced by market maturity and regulations. The results underscore the significance of promoting green finance to bolster investments in sustainable projects and enhance risk management strategies for investors. This research enriches the green finance literature by detailing the financial interconnectedness within the market and providing strategic insights for embedding sustainability in investment portfolios against a backdrop of global economic and geopolitical uncertainties. Research limitations/implications The research highlights the importance of green finance in promoting sustainability and reducing environmental impact. It advocates for regulatory frameworks that support sustainable financial instruments, encouraging the development of financial products aligned with environmental goals and fostering a more sustainable economy. Practical implications These research findings provide actionable guidance for investors and policymakers to develop diversified investment strategies incorporating green bonds and clean cryptocurrencies capable of balancing risks and returns. The study also urges policymakers to establish clear guidelines and incentives for green investments, improving transparency and effectiveness in green finance markets. Originality/value This study uses an innovative TVP-VAR connectedness approach to examine the interactions and spillover effects among clean and dirty cryptocurrencies, green bonds and traditional financial assets. It provides new insights into the roles of green bonds and clean cryptocurrencies as hedging tools in volatile markets, enhancing the understanding of financial interconnectedness and sustainable investment strategies.
Ashimiyu Nafiu, Salaam Olawale Balogun, Courage Oko-Odion, Olanrewaju Olukoya Odumuwagun
The complexities of modern financial markets, characterized by heightened volatility and uncertainty, have necessitated the evolution of advanced risk management strategies. As global markets become increasingly interconnected, financial institutions, investors, and policymakers face unprecedented challenges in identifying, assessing, and mitigating risks. Effective risk management has emerged as a cornerstone of financial stability, requiring a blend of traditional methods and innovative tools. This paper explores comprehensive strategies for navigating volatility in complex financial environments, addressing systemic, credit, market, and operational risks. Traditional approaches, such as portfolio diversification and value-at-risk (VaR) modelling, remain foundational but are now complemented by cutting-edge technologies, including artificial intelligence (AI), machine learning (ML), and big data analytics. These tools enable real-time monitoring, predictive analytics, and stress testing, enhancing the capacity to anticipate and respond to emerging threats. Additionally, the integration of blockchain technology offers improved transparency and resilience in financial transactions, further mitigating systemic vulnerabilities. Case studies from diverse sectors highlight the practical applications of these strategies, illustrating how robust risk management frameworks can minimize losses, enhance profitability, and ensure regulatory compliance. The paper also examines the role of regulatory frameworks in shaping risk management practices and emphasizes the importance of a proactive, adaptive approach in navigating volatile market conditions. By combining traditional methodologies with technological advancements, financial institutions can build resilient systems capable of withstanding shocks and fostering long-term stability. This paper concludes by identifying emerging trends, such as quantum computing and decentralized finance, as transformative forces likely to redefine risk management in the future.
Walid Mensi, Ramzi Nekhili, Xuan Vinh Vo, Sang Hoon Kang
ABSTRACT This paper examines the hourly downward/upward multifractality and dynamic efficiency of four cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC)â before and during the COVIDâ19 pandemic, and during the RussiaâUkraine tension. Using the asymmetric multifractal detrended fluctuation analysis method, the results show significant asymmetric multifractality in all series, which intensifies for BTC only throughout the COVIDâ19 crisis and narrows for ETH, XRP, and LTC. Moreover, we show that cryptocurrency markets are more inefficient during the upward (downward) trend and before (during) the COVIDâ19 crisis. LTC is the least inefficient market pre COVIDâ19, whereas XRP is the least inefficient during the pandemic crisis. The results show evidence of excessive asymmetric multifractality for all four crypto markets. Before the COVIDâ19 crisis, positive values of excess asymmetry in multifractality have been identified for BTC and LTC markets, whereas the excess asymmetry values were negative for ETH and XRP markets. BTC and ETH markets showed wider multifractality fluctuations compared to LTC and XRP, indicating a stronger reaction to the war's impact.
Rudresh Deepak Shirwaikar, Sagar Naik, Abiya Pardeshi, Sailee Manjrekar · 7 authors
No abstract is available for this record.
Yuhan Wang, Di Xiao
No abstract is available for this record.
LuĂs Pedro Freitas, Jorge Cerdeira, Diogo Lourenço
The rise of cryptocurrencies over the past decade has promised to challenge the dominance of fiat money systems and reshape monetary policy. However, recent developments, including market volatility and the collapse of key exchanges like FTX, have eroded public trust, raising skepticism of a feasible transition to a crypto-based monetary system. This paper explores why cryptocurrencies have not met the expectations of their proponents, particularly those who saw them as a step towards Friedrich Hayekâs vision for competitive currency issuance. While cryptocurrencies reflect some aspects of Hayekâs model, their instabilityâespecially in Bitcoin-like assetsâundermines their role as a reliable alternative to fiat money. The paper also considers how central bank independence and regulatory gaps further hinder the development of a robust cryptocurrency framework. Despite the continued relevance of Hayekâs ideas in todayâs monetary landscape, the entrenched structures of modern central banks and the rise of Central Bank Digital Currencies suggest that a decentralised currency order remains unlikely in the near future.
Rafael Baptista Palazzi, Sebastian Schich, Alan Genaro
No abstract is available for this record.
Lihui Tian, Haifeng Wu, Qichang Xie
No abstract is available for this record.
Jieru Wan, Liyan Han, You Wu
No abstract is available for this record.
Botond Benedek, BĂĄlint Zsolt Nagy
Abstract The asymmetries of factors influencing the return of cryptocurrencies have already been well documented; however, in the case of NFTs, only information asymmetries and hedging properties related to asymmetries were studied. Therefore, the present study examines factors affecting NFT returns, from market-related factors (crypto-market index return and stock market index return) to the Amihud illiquidity ratio and Google search trends during different market conditions. The wavelet coherences-based methodology was applied separately during the boom, bust, normal, and turbulent periods identified by structural breakpoints. Based on 14 NFT projects between April 2019 and July 2022, results show two fundamental asymmetries influencing these NFT returns. First, there is an asymmetry in the behavior of the factors in different periods; second, there is an asymmetry in how illiquidity manifests itself over NFTs that do or do not possess cash flow-generating potential.