Sami Mejri, Francisco Jareño, Nasir Khan, Arturo Leccadito
This study examines the impact of geopolitical risk (GPR) on black and green cryptocurrencies during crisis times, focusing on their potential as hedging instruments and safe havens. Using daily data on nine cryptocurrencies (Bitcoin, Ethereum, Binance, Litecoin, Ripple, EOS, IOTA, Stellar and Tezos) and the Geopolitical Risk Index from January 3rd, 2019, to January 20th, 2025, the research employs a Regime-Switching Global Vector Autoregressive (RSGVARX) model and a quantile-on-quantile (QQ) approach to capture heterogeneous responses across market states and quantiles. In addition, the Dynamic Conditional Correlation (DCC) GARCH copula and Dynamic Gerber Correlation (DGC) models assess the hedging effectiveness and optimal portfolio weights of various cryptocurrency pairs. The study uniquely combines the RSGVARX and QQ methods to provide a comprehensive understanding of the dynamic interactions between GPR and cryptocurrency returns and introduces robust portfolio optimisation analysis using advanced econometric models. The results show that the impact of GPR on black cryptocurrencies is generally negative and statistically insignificant in Regime 1, with mixed effects in Regime 2, while green cryptocurrencies show similar heterogeneous responses. Several cryptocurrencies show resilience to GPR shocks in certain scenarios, highlighting their potential as reliable assets in times of geopolitical instability. The portfolio optimisation analysis identifies Bitcoin paired with Ethereum, Binance and Litecoin as the most effective combination for hedging throughout the sample period and during the stressful Russia-Ukraine war and Israeli-Palestinian conflict. These results suggest that investors should consider market states and transition probabilities when developing portfolio strategies involving cryptocurrencies, providing valuable insights for managing risk and ensuring financial stability during geopolitical crises.
The aim of this study is to reveal the dynamics between climate policy uncertainty (CPU) and S&P Global Carbon Credit Index (CARBON), S&P Cryptocurrency DeFi Index (DeFi), and WilderHill New Energy Global Innovation Index (NEX) using data from December 2017 to March 2024 in the US. Fourier Bootstrap ARDL, Fourier Bootstrap quantile causality, and KRLS methods are used in the study. The findings reveal that there is a negative relationship between the CARBON and the CPU index in the long term. Although the DeFi does not have a statistically significant effect in the long term, it reveals that it has a negative effect on the CPU index in the short term. In contrast, the NEX has a positive relationship with the CPU index in both the short and long term. Moreover, there is a U-shaped non-linear relationship between the NEX and the CPU index, which weakens in moderate climate uncertainties and strengthens again in high uncertainty. Considering the causality results, there exists a causality from CARBON to CPU in the 2nd, 3rd, and 4th quantiles, and from CPU to CARBON in the 2nd and 3rd quantiles. Additionally, there is a causality from DeFi to CPU in the 8th quantile and from CPU to DeFi in the 1st quantile. Finally, there is a causal relationship from NEX to CPU in the 2nd, 3rd, 4th, and 5th quantiles and from CPU to NEX in the 9th quantile.
As blockchain technology drives the global expansion of the digital currency market, the widespread adoption of high-frequency trading and cross-market arbitrage strategies poses dual challenges to traditional regulatory measures in terms of timeliness and accuracy. This study constructs a hybrid neural network model that integrates supervised and unsupervised learning to explore multi-dimensional feature fusion paths between on-chain data from blockchain and secondary market price data. Based on dynamic game theory, an intelligent regulatory sandbox system is designed, incorporating on-chain address reputation scoring mechanisms and liquidity smart contract circuit breakers to achieve real-time warnings and responses to market manipulation behaviors. Furthermore,a distributed regulatory framework built on zero-knowledge proof technology is proposed, providing a feasible solution for establishing a penetrating regulatory system while ensuring transaction privacy.
ZaÀfri A. Husodo, Md. Bokhtiar Hasan, Humaira Tahsin Rafia, Masagus M. Ridhwan · 6 authors
This study investigates the interconnected dynamics among diverse digital currencies, specifically focusing on risk-adjusted returns, tail risks, dynamic spillovers, and portfolio implications. Unlike prior research, which typically examines individual digital currency classes separately or in limited combinations, our study integrates six distinct classes of digital currencies, namely Islamic gold-backed cryptocurrencies, green cryptocurrencies, gold-backed stablecoins, non-fungible tokens (NFTs), decentralized finance (DeFi) assets, and conventional cryptocurrencies, enabling direct comparisons of risk-return dynamics and systemic interdependencies. Using Value at Risk (VaR), Conditional Value at Risk (CVaR), quantile-based Vector Autoregression (Quantile VAR), and network connectedness analysis, we provide nuanced insights into the behavior of these assets across various market conditions (bullish, bearish, and normal states). Our results demonstrate that conventional cryptocurrencies and DeFi assets consistently deliver positive risk-adjusted returns, whereas Islamic gold-backed cryptocurrencies exhibit notably higher downside risks and negative performance. Spillover analysis reveals pronounced connectedness, particularly in extreme market states, with conventional cryptocurrencies identified as primary transmitters of market shocks and gold-backed stablecoins and Islamic gold-backed cryptocurrencies as recipients. Our findings underscore significant diversification opportunities offered by pairs of assets exhibiting low connectedness, especially in normal market conditions. Furthermore, portfolio optimization analysis highlights the superior hedging effectiveness and lower hedging costs associated with gold-backed stablecoins and conventional cryptocurrency pairs. This comprehensive investigation delivers critical implications for investors, suggesting informed strategies for asset allocation and risk management. Policymakers can also utilize our insights to design adaptive regulatory frameworks that address systemic risks arising from digital currency markets. ACKNOWLEDGMENT Gazi Salah Uddin gratefully acknowledges the Faculty of Economics and Business, Universitas Indonesia, for the academic appointment as Adjunct and Visiting Professor, and expresses sincere appreciation for the institutional support and research facilities extended during his residency, which significantly contributed to the completion of this work.
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 (2018â2022). 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 RefiningâManufacture 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 parametersâincluding expected returns, volatility, and liquidityâare 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 modelâs 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 blockchainâs emerging applications in sustainable finance and artificial intelligence-driven investment strategies. Despite significant academic contributions, gaps persist, particularly in empirical assessments of blockchainâs 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 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 Trumpâs 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 GĂŒnay, Emrah Ä°smail Ăevik, Mehmet Fatih BuÄan, Sel DibooÄlu · 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.
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 PakĆĄtaitÄ, Ernestas Filatovas, Mindaugas Juodis, Remigijus PaulaviÄius
Bitcoinâs 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 eventsâincluding regulatory changes and institutional announcementsâunderscoring Bitcoinâs evolving market structure. These findings demonstrate that integrating Bayesian MCMC within a regime-switching model provides robust insights into Bitcoinâs deepening connection with traditional financial forces.
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 â namely, the S&P 500, Nasdaq Composite, and Dow Jones Industrial Average â 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 Chaddockâs 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 Bitcoinâs role within the global financial landscape and its potential responsiveness to systemic risks.
This paper investigates Bitcoinâs resilience against the U.S. dollarâwidely recognized as the global reserve currencyâby applying a multi-method wavelet analysis framework to daily price data of Bitcoin, the USD strength index (DXY), the euro, and other assets ranging from August 2015 to June 2024. Quantitative measuresâparticularly the Frobenius norm of wavelet coherence and an exponential decay phase-weighting schemeâreveal that Bitcoinâs out-of-phase relationship with the dollar is lower and more sporadic than that of mainstream assets, indicating it is not tightly governed by dollar fluctuations. Even after controlling for the euroâs dominant influence in the DXY, BTC continues to show weaker coupling than mainstream assetsâreinforcing the idea that it may serve as a partial hedge against dollar-driven volatility. These results support the hypothesis that Bitcoin may serve as a resilient store of value and hedge against dollar-driven market volatility, placing Bitcoin within the broader debate on global monetary frameworks. As global monetary conditions evolve, the resilience of Bitcoin (BTC) relative to the worldâs leading reserve currencyâthe U.S. dollarâhas significant implications for both investors and policymakers.
This study examines whether cryptocurrency markets offer more resilient safe haven properties than gold for stock markets in the BRICS economies from 28th April 2013 to 27th September 2024. Unlike traditional studies that primarily focus on Bitcoin or top-market cap cryptocurrencies , we introduce a novel Crypto index that includes 9468 active and defunct cryptocurrencies, providing a comprehensive view of daily market fluctuations across all listed crypto assets. We also investigate the impact of the Russia-Ukraine military conflict on the safe haven status of these assets. Using a time-varying robust Granger causality framework, we analyse the dynamic relationships between potential safe haven assets and BRICS stocks. Additionally, we explore the network structure of gold, cryptocurrencies, and BRICS stocks across different quantiles . Our results show limited evidence of time-invariant causality, but strong evidence of time-varying causality, suggesting that neither gold nor cryptocurrencies act as safe havens for BRICS stocks over the entire sample period. We find increased market interconnectedness during extreme conditions, with gold and cryptocurrencies initially acting as net receivers of shocks, but gold shifting to a net transmitter during the conflict, indicating stronger safe haven properties for gold. Portfolios favour gold over crypto, and small-cap cryptocurrencies are cheaper but less efficient hedges compared to large-cap cryptos, with Bitcoin emerging as the optimal investment for returns. These findings offer valuable insights for investors and policymakers, particularly for optimizing portfolio management and supporting financial stability during market turbulence.
Ratih Fitria Putri, Robert Marbun, Wulandari Harjanti
The rapid development of cryptocurrency investment has raised concerns regarding its impact on economic stability, particularly in emerging markets. This study employs a qualitative approach through literature review and library research to analyze the risks associated with cryptocurrency investments and their implications for financial stability. This research identifies key risk factors, including market volatility, regulatory uncertainty, cybersecurity threats, and financial system disruptions by examining existing scholarly works, regulatory frameworks, and market trends. The findings indicate that cryptocurrency investments offer opportunities for financial inclusion and economic diversification but also pose significant risks to economic stability due to price fluctuations and speculative behavior. Furthermore, the lack of a unified regulatory framework across different countries exacerbates these risks, leading to potential financial instability in emerging economies. The study highlights the necessity of regulatory intervention and policy formulation to mitigate these risks while harnessing the benefits of cryptocurrency investments. Governments and financial institutions in emerging markets must establish robust risk management strategies and regulatory frameworks to balance innovation and financial stability. This research contributes to the academic discourse by providing a comprehensive understanding of the relationship between cryptocurrency investments and economic stability in emerging markets. Future research should focus on empirical case studies to further explore the long-term effects of cryptocurrency investments on financial stability.
Pooja Kumari, Amit Shankar, Rsha Alghafes, Laura Broccardo · 5 authors
ABSTRACT This study examined how Bitcoin, energy prices, and geopolitical risk interact by examining the first four moments (mean, variance, skewness, and kurtosis) of their return distributions by using wavelet analysis. The findings reveal that the coâmovement patterns of energy index, geopolitical risk index, and Bitcoin prices are time and frequency sensitive. During the turbulent period of 2020â2024, significant cross effect was observed at mediumâ and longâterm time scales in the relationship between the energy index and the geopolitical risk index. Similarly, in the case of Bitcoin and the geopolitical risk index, significant crossâeffects were detected at mediumâ and shortâterm time scales. From 2021 onwards, a strong coherence is observed at high and medium frequencies for all four moment pairs among Bitcoin, energy prices, and geopolitical risk. In terms of the Bitcoinâenergy relationship, significant coâmovement in mean and volatility is noted throughout most of the sample period and across different frequency bands. Moreover, crossâskewness and crossâkurtosis connections are more prominent at shortâ and mediumâterm horizons, especially during covid pandemic. These insights are valuable for investors and policymakers in risk management.
Suwan Long, Ying Xie, Zhengyuan Zhou, Brian M. Lucey · 5 authors
This paper examines the relationship between cryptocurrency market dynamics and investor sentiment, employing advanced techniques like time-variant Granger causality and asymmetric time-varying parameter vector autoregression (TVP-VAR) frequency connectivity. We create unique sentiment analysis tools, including a custom cryptocurrency sentiment lexicon, to deeply analyze content in the cryptocurrency domain, particularly focusing on investor discussions and viewpoints. Our findings demonstrate a significant, evolving link between market sentiment and cryptocurrency movements. A key observation is that the volatility of shock transmission is tightly connected to major market events, often influenced by large-scale investors, or âwhalesâ. Our study indicates that market sentiment consistently affects both short- and long-term cryptocurrency volatility, underlining the crucial influence of investor sentiment in driving the dynamics of the cryptocurrency market. This underscores the importance of understanding investor sentiment for predicting and navigating the cryptocurrency market.
This study examines the impact of the coronavirus disease 2019 (COVID-19) pandemic on market efficiency by analyzing three time series -- price returns, absolute returns, and volatility increments -- in stock (Deutscher Aktienindex, Nikkei 225, Shanghai Stock Exchange (SSE), and Volatility Index) and cryptocurrency (Bitcoin and Ethereum) markets. The effect is found to vary by asset class and market. In the stock market, while the pandemic did not influence the Hurst exponent of volatility increments, it affected that of returns and absolute returns (except in the SSE, where returns remained unaffected). In the cryptocurrency market, the pandemic did not alter the Hurst exponent for any time series but influenced the strength of multifractality in returns and absolute returns. Some Hurst exponent time series exhibited a gradual decline over time, complicating the assessment of pandemic-related effects. Consequently, segmented analyses by pandemic periods may erroneously suggest an impact, warranting caution in period-based studies.