This study examines the impact of the Financial Stress Index (FSI) and US Equity Market Uncertainty (EMU) on cryptocurrencies. We analyse the short and long-run impact of FSI on prices of the top five cryptos using the Nonlinear ARDL (NARDL) framework to assess the alternative asset suitability of these cryptocurrencies during different financial market stress events. Our analysis finds a statistically significant impact of FSI and EMU on the cryptocurrency returns for both short-run and long-run. While the impact of FSI is asymmetric in the long run, we find that in the short run, the impact is symmetric. Thus, a rise in the FSI has a larger impact on the returns when compared to a fall in the FSI in the long run. The findings across various subperiods suggest that FSI and EMU affect the returns of cryptos differently. While in some periods, we see that a surge in financial stress leads to an increase in returns for some of the cryptos, for others, it leads to a decrease in returns. This indicates that the investors do not have the same preference for all the cryptos during periods of heightened financial stress and they may not be considered equal safe havens. Our results have clear policy implications for investors, regulators, and policymakers.
General background: The increasing integration of digital technologies has transformed global financial systems, with cryptocurrencies, especially Bitcoin, emerging as prominent financial instruments. Specific background: Amid widespread adoption by institutions and individuals, Bitcoin has garnered attention for its potential to influence traditional financial markets, particularly during periods of global uncertainty such as the COVID-19 pandemic. Knowledge gap: While much has been discussed about the theoretical influence of cryptocurrencies, empirical evidence on their actual impact on global financial indices remains inconclusive. Aims: This study investigates the effect of Bitcoin trading volume and the COVID-19 pandemic on a composite index comprising advanced (S&P 500), emerging (KLSE), and developing (DZ) market indices from July 2018 to December 2022. Results: Using a fixed-effects panel data model, the findings reveal that past market performance significantly predicts current performance, while Bitcoin trading volume and the pandemic show no statistically significant impact. Novelty: The study uniquely combines market classifications and utilizes a composite index to empirically isolate the influence of Bitcoin across diverse economies. Implications: These results suggest that, despite Bitcoin's rising prominence, its direct influence on global financial markets may be limited in the short term, underscoring the need for continued investigation as regulatory frameworks and adoption rates evoHighlight : Minimal Impact: Bitcoin trading volume and the COVID-19 pandemic had no statistically significant effect on global financial market indices (2018–2022). Strong Market Correlation: Global financial indices showed strong interdependence, reflecting synchronized market behavior. Future Outlook: Despite current findings, evolving crypto regulations and technologies may alter their financial market influence. Keywords : Cryptocurrencies, Bitcoin, Trading Volume, COVID-19, Financial Indices
ABSTRACT This research examined the connection between Bitcoin, the prominent and extensively mined cryptocurrency, and CO 2 emissions using the SVAR model. Azerbaijan, Kazakhstan, and Russia, the three main countries in the Caspian Basin that are the centre of cryptocurrency mining, were examined in terms of their primary industries. The variance decomposition analysis indicated that the Bitcoin price had the most significant explanatory role in CO 2 emissions released by Oil and Natural Gas industry in Azerbaijan. When it comes to the CO 2 emissions that were emitted by the Petroleum Refining‐Manufacture of Solid Fuels and Other Energy industry, as well as Manufacturing Industries and Construction, the Bitcoin price had the most important effect in Kazakhstan. There was a significant contribution made by Bitcoin to the CO 2 emissions that were emitted by the Manufacturing Industries and Construction in Russia. The impulse response functions illustrated a strong association between Bitcoin and CO 2 emissions. However, in contrast to existing research, this relationship was found to be negative. The increase in energy usage during Bitcoin price falls can be attributed to the need to compensate for losses, particularly in the mining process. To diminish this connection, the dependence of the cryptocurrency on fossil fuels must be minimised.
This paper introduces a novel multi-objective optimization framework for sustainable portfolio rebalancing under uncertainty. The model simultaneously targets return maximization, downside risk control, and liquidity preservation, addressing the complex trade-offs faced by investors in volatile markets. Unlike traditional static approaches, the framework allows for dynamic asset reallocation and explicitly incorporates nonlinear transaction costs, offering a more realistic representation of trading frictions. Key financial parameters—including expected returns, volatility, and liquidity—are modeled using interval arithmetic, enabling a flexible, distribution-free depiction of uncertainty. Risk is measured through semi-absolute deviation, providing a more intuitive and robust assessment of downside exposure compared to classical variance. A core innovation lies in the behavioral modeling of investor preferences, operationalized through three strategic configurations, pessimistic, optimistic, and mixed, implemented via convex combinations of interval bounds. The framework is empirically validated using a diversified cryptocurrency portfolio consisting of Bitcoin, Ethereum, Solana, and Binance Coin, observed over a six-month period. The simulation results confirm the model’s adaptability to shifting market conditions and investor sentiment, consistently generating stable and diversified allocations. Beyond its technical rigor, the proposed framework aligns with sustainability principles by enhancing portfolio resilience, minimizing systemic concentration risks, and supporting long-term decision-making in uncertain financial environments. Its integrated design makes it particularly suitable for modern asset management contexts that require flexibility, robustness, and alignment with responsible investment practices.
Blockchain technology has emerged as a revolutionary force in modern finance, significantly impacting financial market efficiency by enhancing transparency, reducing transaction costs, and eliminating intermediaries. However, its overall effect on market efficiency remains a subject of academic debate. This study conducts a bibliometric and network analysis to systematically assess the evolution of blockchain research in financial markets, highlighting key publication trends, influential authors, leading institutions, and dominant research themes. Using Scopus as the primary database, a structured search strategy identified 3,054 high-quality articles published between 2005 and 2025, focusing on Business, Management, and Accounting (BUSI) and Economics, Econometrics, and Finance (ECON). VOSviewer was employed to map research collaborations, co-authorship structures, and keyword co-occurrences, providing a comprehensive understanding of the intellectual development in this field. Findings reveal a sharp increase in blockchain-related financial research, particularly post-2016, driven by the expansion of decentralized finance (DeFi) and institutional interest in digital assets. The study identifies Corbet, S., and Yarovaya, L., among the most influential authors, while leading institutions include Dublin City University and Lebanese American University. China, the United States, and India dominate research output, reflecting global interest in blockchain's financial implications. The analysis further uncovers key thematic clusters, including market efficiency, liquidity, and regulatory challenges, while also highlighting blockchain’s emerging applications in sustainable finance and artificial intelligence-driven investment strategies. Despite significant academic contributions, gaps persist, particularly in empirical assessments of blockchain’s long-term impact on market stability, regulatory alignment, and integration with traditional financial systems. Future research should focus on addressing these gaps by exploring cross-border regulatory frameworks, expanding studies beyond cryptocurrencies to tokenized assets, and investigating the role of artificial intelligence in blockchain-based financial solutions. By advancing these research directions, scholars and policymakers can develop a structured approach to blockchain adoption, ensuring its long-term sustainability and effectiveness in global financial markets.
This chapter provides a comprehensive overview of the cryptocurrency ecosystem. It begins by tracing the market's origins, focusing on Bitcoin's 2009 launch and its transformative impact on digital transactions. Blockchain technology is explained in detail, covering its decentralized ledger system, including blocks, chains, nodes, and consensus mechanisms like Proof of Work and Proof of Stake. The chapter offers a historical perspective on mining, from early methods to advanced techniques. The workings of cryptocurrency exchanges are discussed, comparing centralized and decentralized platforms. The chapter examines cryptocurrency price determination through supply and demand dynamics and external influences such as economic trends and technological advancements etc. Market volatility and its implications for investors are analyzed, along with global regulatory approaches and their effects on the market. The chapter concludes with insights into future trends and emerging applications, offering a thorough understanding of the cryptocurrency market and its evolution.
Nghiên cứu nhằm mục tiêu phân tích mối liên hệ giữa thị trường Non-Fungible Tokens (NFT-Coin) và thị trường chứng khoán Việt Nam (chỉ số VNI) trong giai đoạn 2019-2024 bằng dữ liệu tỷ suất lợi nhuận theo ngày. Để thực hiện mục tiêu nghiên cứu, nhóm tác giả sử dụng phân tích Wavelet và kiểm định nhân quả để tiếp cận mối liên hệ trên bằng phân tích tương quan - nhân quả trên các miền tần số khác nhau. Kết quả nghiên cứu cho thấy, tồn tại tương quan thấp giữa biến động NFT-Coin và chỉ số VNI với hệ số tương quan chủ yếu dao động trong khoảng từ -0,2 đến 0,2). Trong đó, tồn tại sự gắn kết yếu giữa NFT-Coin và chỉ số VNI trên miền tần số (2-16) ngày và chặt chẽ hơn trên miền tần số (16-32) ngày. Sau đó, kiểm định nhân quả phi tuyến chỉ ra rằng việc dự báo biến động chỉ số VNI bằng biến động của các NFT-Coin là rất hạn chế trên miền tần số (2-8) ngày trong cả giai đoạn và trên tất cả miền tần số trong giai đoạn xung đột Nga - Ukraine.
This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon). Using comparative analysis of ARCH(1), GARCH(1,1), and EGARCH(1,1,1) models, the study evaluates their predictive accuracy through multiple metrics including AIC, MAE, RMSE, and SMAPE. Results indicate that EGARCH(1,1,1) generally performs better for cryptocurrencies and U.S. stocks, while GARCH(1,1) shows superior performance for Indonesian stocks, suggesting that volatility patterns and optimal forecasting models vary across different market contexts.
Abstract This study offers a detailed literature review and bibliometric analysis of cryptocurrency, virtual digital assets (VDA), and distributed ledger technology (DLT)-based digital currencies. We analyze current research and publishing trends, particularly in forecasting cryptocurrency price volatility. The paper categorizes the development and maturity of various analytic methods employed across domains like centralized finance, decentralized finance, and blockchain. The review highlights both traditional econometric models and emerging machine learning algorithms in these areas, illustrating their evolution and application depth in the literature. Our research further explores national policies on VDA and DLT, focusing on India. We employ sentiment analysis to assess the tone and implications of Indian legislation, policies, and court orders concerning VDA. The analysis reveals a predominantly neutral stance, with a notable positive tilt, suggesting a favorable sentiment from Indian authorities towards these emerging technologies. The sentiment distribution also shows that the Indian authorities are anticipatory and expressing trust in their policies and regulations. Although policy frameworks are still evolving, efforts show a drive towards creating a safe, inclusive environment for VDA and DLT applications in India. Finally, we identify existing research gaps, propose theoretical questions, and recommend potential directions for national-level policies based on our findings.
Our study analyzes the combined impact of geopolitical risks and investor sentiment on the major cryptocurrencies, Bitcoin and Ethereum, using monthly data from December 1, 2020, to the end of April 2025. Through a rigorous econometric approach-including unit root tests (Dickey-Fuller (1979-1981) and Perron (1998)), cointegration techniques (Engle and Granger (1987) and Johansen (1990)), and error correction models (ECM and VECM)-we examined the long- and short-term dynamics between cryptocurrencies and three indices: investor sentiment, crypto market sentiment, and the composite geopolitical risk index. Our results confirm the existence of cointegration relationships between these crypto-assets and the indices, indicating structural interdependence during periods of global uncertainty. In the short term, fluctuations in investor sentiment and geopolitical risks significantly affect the returns of Bitcoin and Ethereum, with a rapid adjustment toward long-term equilibrium. Moreover, Ethereum appears to be slightly more sensitive to emotional and geopolitical shocks than Bitcoin. However, our study has certain limitations, notably the use of composite indices that may not capture all the qualitative nuances of the phenomena studied and the assumption of linearity in the modeled relationships. For future research, we suggest integrating nonlinear models and leveraging real-time sentiment data derived from artificial intelligence, as well as expanding the analysis to other segments of the crypto-asset market. Ultimately, our study enhances the understanding of exogenous factors influencing cryptocurrencies in an unstable global environment.
We analyze Trump’s memecoin launch, showing heterogeneous volatility spillovers driven by sentiment and fundamentals. Political signals amplified speculative dynamics, underscoring how politics increasingly shapes cryptocurrency markets and investor behavior.
Samet Günay, Emrah İsmail Çevik, Mehmet Fatih Buğan, Sel Dibooğlu · 5 authors
Abstract Utilizing blockchain technology is transforming traditional business practices into a new paradigm, giving rise to what we refer to as blockchained models. This paper uses wavelet coherence analysis to identify the connectedness of blockchained sectoral indices with Bitcoin and the Fear and Greed Index that represents investor sentiment in the cryptocurrency market. Results show persistent and positive correlations between sector returns and investor sentiment and sectoral return series lead investor sentiment. The relationship between Bitcoin and sectoral indices is consistent for return series and suggests an in-phase (positive) relationship between these variables at all frequencies. We usually have found negative correlations for the co-movements of investor sentiment and sectoral volatility, where investor sentiment leads to sector return volatilities. The application of blockchain technology across various sectors, coupled with the proliferation of altcoins, appears to drive distinct price developments in these cryptocurrency sectors. These developments are predominantly influenced by sentimental factors, often diverging from the trends of Bitcoin.
David Umoru, Malachy Ashywel Ugbaka, Anake Fidelis Atseye, Samuel Manyo Takon · 18 authors
The financial market is a decentralized market made up of global network of businesses, forex, stock investment, and digital markets. The paper evaluated the patterns and interrelationships of volatilities in return amongst foreign exchange, stock, and bitcoin markets returns in oil importing nations. The Markov-Switching and quantile regression estimation methods were executed. Results indicate stock markets of Kenya and Uganda had the most frequent depreciating returns. Bitcoin returns were negatively and significantly influenced by changes in currency values, whereas change in bitcoin trading value causes a higher change in exchange rate returns. A percentage increase in stock market returns stimulates exchange rate returns to rise also but at a higher rate. Returns on exchange rates and Bitcoin markets are significant predictors of stock market returns. Exchange rate volatility dynamics occur in the opposite direction as those in stock markets and in the floor of Bitcoin market. Volatility was significantly observed when currency devalued confirming the erratic behaviors of investors to dwindling local currency values compared to the U.S. dollar. Financial markets authorities can use the research findings to support their choice to regulate the financial markets and shield investors from information asymmetry that could result from cross-market volatility interrelationships.
Rihab Belguith, Hind Alnafisah, Yasmine Snene Manzli, Ahmed Jeribi
This study investigates how gold and Bitcoin can mitigate risk for investors in the BRICS Plus economies (Brazil, Russia, India, China, South Africa, and invited members: Egypt, Argentina, Saudi Arabia, and the United Arab Emirates). We employ a time-varying copula approach to analyze the safe haven, hedging, and diversification abilities of these assets against the BRICS Plus stock market indices for the period from January 4, 2016, to January 5, 2024. This timeframe encompasses significant events including the COVID-19 pandemic, the Russia–Ukraine conflict, and the Silicon Valley Bank collapse. Results show that during normal periods, both gold and Bitcoin act as diversifiers. However, during crises, their dynamics change, revealing their effectiveness as risk mitigators. Gold emerges as a strong diversifier and safe haven, particularly for Russia, India, Argentina, Saudi Arabia, and the United Arab Emirates during the COVID-19 pandemic. Bitcoin exhibits some safe-haven qualities, especially for South Africa and India, but its effectiveness varies by country. Our research suggests gold is a more consistent hedge than Bitcoin, especially during market downturns. This study offers practical insights for investors and policymakers. Investors in the BRICS Plus region can leverage these findings to make informed decisions by choosing between the stability of gold and the potential diversification of Bitcoin. Policymakers, on the other hand, can use this knowledge to develop strategies that help manage risk during volatile market conditions.
This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Vaiva Pakštaitė, Ernestas Filatovas, Mindaugas Juodis, Remigijus Paulavičius
Bitcoin’s role in global finance has rapidly expanded with increasing institutional participation, prompting new questions about its linkage to macroeconomic variables. This study thoughtfully integrates a Bayesian Markov Chain Monte Carlo (MCMC) covariate selection process within homogeneous and non-homogeneous Hidden Markov Models (HMMs) to analyze 16 macroeconomic and Bitcoin-specific factors from 2016 to 2024. The proposed method integrates likelihood penalties to refine variable selection and employs a rolling-window bootstrap procedure for 1-, 5-, and 30-step-ahead forecasting. Results indicate a fundamental shift: while early Bitcoin pricing was primarily driven by technical and supply-side factors (e.g., halving cycles, trading volume), later periods exhibit stronger ties to macroeconomic indicators such as exchange rates and major stock indices. Heightened volatility aligns with significant events—including regulatory changes and institutional announcements—underscoring Bitcoin’s evolving market structure. These findings demonstrate that integrating Bayesian MCMC within a regime-switching model provides robust insights into Bitcoin’s deepening connection with traditional financial forces.
This study delves into the transformative role of digital currencies in reshaping global trade, financial systems, and regulatory frameworks. It investigates the integration of Central Bank Digital Currencies (CBDCs), cryptocurrencies, and stablecoins into the global economy, exploring their potential to streamline cross-border transactions, reduce costs, and enhance financial inclusion. Through case studies from China, El Salvador, and the European Union, the study examines the practical implications and challenges associated with the widespread adoption of digital currencies. The research also scrutinizes the regulatory complexities, including the need for international harmonization of legal standards to mitigate risks such as money laundering and fraud. Additionally, the study highlights the technological innovations inherent in blockchain and distributed ledger technologies (DLT), which promise to enhance transparency, security, and operational efficiency.
Economic, Social, and Public Health Issues in Russia and Globally
This article examines the influence of the major stock indices' value on Bitcoin price fluctuations. The study focuses on the correlation between the absolute value of Bitcoin and the daily closing prices of leading stock market indices — namely, the S&P 500, Nasdaq Composite, and Dow Jones Industrial Average — based on daily time series data from 2020 to 2025. The paper spotlights the correlation analysis between the dynamics of daily changes (increases or decreases) in the closing prices of these indices and the corresponding changes in the price of Bitcoin over the same time frame. The Pearson correlation coefficient was employed to quantify the strength and direction of linear relationships between Bitcoin and each stock market index for each year within selected timeframe. Furthermore, the correlation results were interpreted using Chaddock’s scale to assess their practical significance and to identify the stock asset with the highest correlation to Bitcoin. The methodological framework of the study includes quantitative analysis methods, observation, and comparative analysis. The findings reveal the presence of a variable, time-dependent correlation between Bitcoin and the selected stock indices, with stronger interconnections observed during periods of global financial turmoil. This may reflect a growing integration of the cryptocurrency market into the broader financial system. At the same time, during relatively stable periods, both the cryptocurrency and traditional stock markets tend to show a degree of independence from each other. These results support the hypothesis of Bitcoin's complex and hybrid nature as a financial asset, which may simultaneously serve speculative purposes and display certain hedging characteristics. The results obtained may be of practical importance for the development of investment strategies, portfolio diversification, cryptocurrency price forecasting, and risk assessment in conditions of increasing financial market uncertainty. Consequently, the research contributes to a deeper understanding of Bitcoin’s role within the global financial landscape and its potential responsiveness to systemic risks.