Purpose The study aimed to determine the static return connectedness between Brazil, Russia, India, China and South Africa (BRICS) equity markets and crypto assets. Design/methodology/approach The study employs the time-varying parameter vector autoregression (TVP-VAR) method to examine the static and dynamic connectedness between crypto assets and the BRICS stock market. The study sample size was segmented into full sample, pre-COVID-19 and post-COVID-19 for in-depth analysis. Findings Empirical findings pointed out the significant rise in the total connectedness between both markets in the pre-COVID-19 period. Our result also exhibits a lower level of connectedness during the post-COVID-19 period. During the full sample period, it was found that cryptocurrencies and Indian, Chinese and South African stock markets remained key return transmitters, while Russian and Brazilian stock markets were seen as recipients. Moreover, during the pre-COVID period, cryptocurrencies played the role of return transmitter while the stock markets in BRICS remained recipients of return spillover. Practical implications This study contains practical insights for investors and portfolio managers in diversifying their portfolios considering the aforementioned connectivity of both markets, especially during periods of instability. Originality/value The study highlighted the importance of the TVP-VAR method in analyzing the static and dynamic connectedness of returns between cryptocurrencies and BRICS stock markets in different periods, including pre- and post-COVID-19. It further pragmatized the dynamic roles of cryptocurrencies as transmitters of returns and the BRICS stock markets as receivers where investors and policymakers can navigate market uncertainties.
M. Venturini, Daniel GarcĂa-Costa, Elena Ălvarez-GarcĂa, Francisco Grimaldo · 5 authors
Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than \$3.50 trillion, with 1 Bitcoin exchanging for more than \$97,000 at the end of November 2024. Monitoring the evolution of these systems is key to understanding whether the popular perception of cryptocurrencies as a new, sustainable economic infrastructure is well-founded. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.
We explore volatility spillover effects between mainstream cryptocurrencies and energy token markets in the 120 days following three notable Blockchain bridge heists in 2022. Using the DCC-GARCH model, we find significant spillover effects between Bitcoin, Ethereum, and energy tokens like Power Ledger Token and Energy Web Token post-heists. This indicates heightened investor concern and panic trading impacting cryptocurrencies and energy token markets. Our analysis also reveals a herding behaviour in energy tokens under market stress and increased liquidity issues, leading to broader market quality deterioration. Based on these findings, we propose regulatory enhancements and the âEnergy Future Fundâ to support the stability and growth of energy token markets.
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.
The purpose of this article is to study analysis the Decentralized Finance (DeFi) literature. By synthesizing the themes and theorical frameworks, we aim to identify knowledge gaps and potential areas for future research in the DeFi landscape. We conduct bibliometric and content analysis on a corpus of 275 articles extracted from the Web of Science and Scopus databases. We use the Bibliometrix package in R software to apply co-citation and bibliographic coupling. We find three research clusters (a) socioeconomic (b) technology and (c) financial with their conceptual structure, interactions and transformations. Applying both co-citation and bibliographic coupling network analysis yields a dynamic view of the field tracing thematic evolution from its inception to the present day, revealing a decline in academic interest in DeFi security vulnerabilities in contrast to the growing emphasis on social media's influence on DeFi prices.
Alparslan Sari, Mehmet A Gavcar, Safak Aplay, Adnan Ăzsoy
This study rigorously investigates the application and effectiveness of automated trading bots in cryptocurrency markets, with a particular focus on the deployment and performance of key strategies such as Mean Reversion, Arbitrage, and Grid Trading. Leveraging the CCXT library to access real-time market data from a variety of cryptocurrency exchanges, this research aims to analyze the operational dynamics and strategic efficacy of these bots under different market conditions. Through detailed simulations and comprehensive data analysis, the study evaluates the bots' ability to adapt to and capitalize on market anomalies and fluctuations. The findings are expected to provide valuable insights into the potential and limitations of each strategy, contributing to the advancement of trading bot technology and the optimization of trading strategies.
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.
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.
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.
Abstract This proposal introduces a novel decision-making framework to advance safe economic activities in cyberspace. We focus on identifying anomalies within crypto-asset trading, recognized as potential sources of criminal activity, severely undermining the credibility of such assets. Detecting and mitigating such anomalies holds significant societal implications, particularly in fostering trust within blockchain networks. We aim to bolster the âsocial trustâ inherent to blockchain technology by facilitating informed economic activities in cyberspace. To achieve this, we propose integrating two artificial intelligence (AI) systems into a blockchain-based decentralized autonomous organization (DAO). The first AI application involves amalgamating various anomaly indicators, spanning from cluster coefficient, entropy, triangular motif analysis, correlation tensor analysis, loop component by Hodge decomposition, loop causality detection, network classification using graph Laplacian, and persistent homology analysis, into a comprehensive indicator using a Boltzmann machine. The second AI application entails deploying conversational AI to guide and support traders, aiding them in making informed trading decisions. This system is designed to alert DAO members to anomalies based on the integrated indicators, especially during massive price fluctuations. We operate under the assumption of close collaboration between governments, experts, traders, system developers, and operators to effectively organize DAOs. The primary technical challenge in our proposal lies in developing a wallet assisted by an intelligent software agent capable of safe interactions with traders within a unified DAO. With this organization, we envision fostering a global economic ecosystem where physical and cyber worlds converge, allowing democratic economic participation.
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.
Cryptocurrencies have introduced a transformative paradigm in financial technology, challenging traditional financial structures and creating novel transactional frameworks. With the rapid expansion of the cryptocurrency market, the need for objective assessment and comparative analysis of leading digital assets has become increasingly pertinent. This study presents a detailed, data-driven evaluation of five prominent cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Tether (USDT), USD Coin, and Lido Staked Ether (STETH). Drawing on an extensive dataset sourced from IntoTheBlock, a leading platform for cryptocurrency analytics, we assess these cryptocurrencies based on selected efficiency indicators. Our research methodology encompasses a systematic exploration of financial and network metrics, including market capitalization, volatility, daily active addresses, and transaction statistics. The results provide nuanced insights into the relative performance of these assets, identifying Bitcoin as the most efficient based on the selected criteria. This work emphasizes the significance of empirical, data-centric methodologies, eschewing subjective judgments, to deliver actionable insights for investors, policymakers, and scholars in the domain of decentralized finance.
This study examines the volatility dynamics of Bitcoin (BTC-USD) and MicroStrategy (MSTR) from September 2019 to September 2024 using the GARCH (1,1) model. Volatility is a key measure of risk in the financial market, understanding its patterns is crucial for effective portfolio management, risk management, and corporate financial strategies. Bitcoin, while known to be volatile, is very unpredictable, and given the high holding of that on MicroStrategy's balance sheet, it is closely tagged to the volatility of Bitcoin. Critical periods, such as the COVID-19 and the subsequent crypto market downturn between 2022 and 2023, demonstrate the linkages between traditional equities and digital assets. The findings of such analysis will prove that MicroStrategy's volatility has indeed closely followed the footsteps of Bitcoin, especially during the 2024 rally in that market, including all its shocks and recoveries. These find great importance in understanding volatility due to the growing integration of digital assets into corporate portfolios. This research will offer investors and corporate managers alike extensive insight into risk management and portfolio diversification by accounting for volatility dynamics between cryptocurrencies and stocks.