The development of the cryptocurrency segment within the global financial market has emerged as one of the most transformative phenomena of the digital economy over the past decade. The present study aims to analyse the global imperatives driving this development, focusing on the key trends, challenges, and opportunities shaping the cryptocurrency market. Methodology. This study uses a combination of analytical and comparative methodologies to examine the cryptocurrency segment within the global financial market. The analytical approach is used to assess the structural dynamics, market trends and capitalisation growth of cryptocurrencies, while the comparative method facilitates the assessment of differences and similarities in the adoption of cryptocurrencies across different countries and financial systems. Data was collected by reviewing publicly available financial reports, cryptocurrency market data and institutional studies. Quantitative analysis was performed to evaluate numerical trends in market capitalisation, transaction volumes, and cryptocurrency usage in payment systems. Furthermore, a qualitative analysis was conducted to elucidate the regulatory challenges and their ramifications for financial stability. Results. The findings indicate the preeminence of Bitcoin, its evolution into a global asset, and the expanding role of altcoins, utility tokens and stablecoins. The analysis reveals the rising use of cryptocurrencies in commercial payments, the issuance of national digital currencies, and the substantial adoption of blockchain technologies by global corporations. However, the study also identifies critical challenges, including regulatory ambiguities, security vulnerabilities, and systemic risks associated with financial stability. The value and originality of this research lie in its comprehensive approach to assessing the multifaceted nature of the cryptocurrency market. The integration of quantitative insights with policy implications has resulted in the formulation of a novel framework for comprehending the strategic role of cryptocurrencies in the evolving global financial landscape. The study's findings offer actionable recommendations for policymakers, investors, and financial institutions seeking to navigate the intricacies of the cryptocurrency ecosystem.
Aktham Maghyereh, Mohammad AlâShboul, Basel Awartani
Research background: This paper explores the hedging and safe-haven properties of gold-backed cryptocurrencies within the context of conventional cryptocurrencies such as Bitcoin, Ethereum, Tether, and Binance. With the rise of blockchain technology, cryptocurrencies have gained recognition as alternative investment assets, drawing comparisons to traditional safe-haven assets like gold. However, the risk management potential of crypto gold, especially during periods of extreme market volatility, remains under-examined. Purpose of the article: The purpose of this article is to assess the effectiveness of gold-backed cryptocurrencies as hedging instruments and safe havens for investors in conventional cryptocurrencies. By analyzing their tail dependence during extreme market fluctuations, the study aims to determine their risk management utility. Methods: To achieve this, we employ a Studentâs t copula structure integrated with an ARMA-GJR-GARCH model to measure the time-varying tail dependence between gold-backed and conventional cryptocurrencies. This approach allows for a comprehensive analysis of both normal and extreme market conditions. We use the Digix Gold Token (DGX) as a representative of gold-backed cryptocurrencies. The study examines four major conventional cryptocurrencies â Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance (BNB) â by analyzing daily closing prices from May 14, 2018, to January 31, 2023, which comprise 1702 observations. The dataset, sourced from coincodex.com, includes periods of significant market stress, such as the COVID-19 pandemic and the Russian-Ukrainian conflict. Findings & value added: The findings reveal a weak association between gold-backed cryptocurrencies and conventional cryptocurrencies, resulting in medium-to-low hedging effectiveness during the sample period. Nevertheless, during crisis periods, a negative association is observed, indicating that gold-backed cryptocurrencies act as effective safe havens in times of market distress. The study contributes to the literature by providing empirical evidence on the risk management benefits of crypto gold, particularly during financial crises, and highlights its potential inclusion in portfolios with cryptocurrency investments to enhance resilience.
Weiwei Guo, Hossein Jahanshahloo, Laima Spokeviciute, Qingwei Wang
This paper examines how on-chain factors (number of active wallets, transaction fees, and transaction volume) and off-chain factors (liquidity and investor attention) impact Bitcoin market efficiency from April 2014 to April 2022. We identify three periods in Bitcoinâs market development: development, growth, and additional development stage. We propose three hypotheses: (1) increased investor attention enhances market efficiency, (2) a rise in active users improves efficiency directly and through liquidity and investor attention, and (3) higher transaction fees and on-chain volume positively impact efficiency directly and indirectly. Our findings support these hypotheses during Bitcoinâs development and growth periods. However, in the additional development stage, the total effect of active users, transaction fees, and transaction volume becomes negative when considering mediating effects, and largely insignificant when focusing on direct effects. Additionally, we find increased netflow between whales and exchanges, a proxy for institutional activity, improves efficiency. We conclude that as Bitcoinâs market develops, factors such as changing user composition and increased regulatory scrutiny alter the dynamics of on-chain factors and their influence on market efficiency.
Purpose This study aims to examine the impact of climate-related risks on cryptocurrency volatility during crisis periods, focusing on the physical risk index (PRI) and transition risk index (TRI). It investigates how acute and chronic climate events, alongside regulatory and technological changes, influence market dynamics in major cryptocurrencies, including Bitcoin, Ethereum, Litecoin and Ripple. Design/methodology/approach A fuzzy logic model is employed to evaluate the effects of PRI and TRI on cryptocurrency volatility. The modelâs accuracy is validated using root mean square error (RMSE) metrics to ensure reliability. Findings The results reveal that acute events (e.g. hurricanes and wildfires) and chronic risks (e.g. long-term environmental disruptions) significantly heighten cryptocurrency volatility. Transition risks, including regulatory and technological shifts, also play a pivotal role. Bitcoin and Ethereum exhibit the highest sensitivities, reflecting the critical influence of climate risks on market stability. Research limitations/implications This study enriches the literature by integrating climate risk factors into cryptocurrency market analysis and advancing fuzzy logic models to assess non-linear interactions in financial markets. It provides a novel framework for evaluating external shocksâ impact on digital assets. Practical implications Investors and market participants can use these findings to incorporate climate risks into their investment strategies, diversify portfolios and anticipate periods of instability. The insights also guide policymakers in developing resilient frameworks that align cryptocurrency regulations with environmental goals. Social implications By linking climate risks to cryptocurrency market behavior, this research emphasizes the need for sustainable investment practices and collaborative policy efforts. It advocates for integrating environmental sustainability into financial systems to mitigate systemic risks and promote economic resilience. Originality/value This research is among the first to apply PRI and TRI within a fuzzy logic framework to cryptocurrency markets, offering new insights into how climate risks drive financial volatility during crisis periods.
Since its creation in 2008, Bitcoin has often been compared to precious metals due to their shared characteristics as safe havens, hedges, and risk diversification tools. This study uses the DCC-GARCH model to analyze dynamic conditional correlations and volatility spillovers between Bitcoin and the returns of gold, copper, silver, and platinum. The findings reveal persistent volatility and clustering in the returns of both Bitcoin and these metals. There is a one-way volatility spillover from gold to Bitcoin, and from Bitcoin to copper, silver, and platinum. Significant dynamic conditional correlations are observed between Bitcoin and both gold and copper, while no significant correlations are found with silver and platinum. These results provide valuable insights for portfolio diversification strategies and inform policymaker decisions in financial markets.
This study investigates the causal relationships between Bitcoin and the US Dollar (USD), Gold, and BIST100 Index as alternative investment instruments. Employing Hongâs variance causality test, the research explores spillover effects in mean and volatility. Using daily data from September 17, 2014, to October 13, 2023, the study reveals a one-way average causality from Bitcoin to BIST100 and the USD. Variance test results show a two-way volatility spillover between Bitcoin and USD, Gold, and BIST100. Hacker-Hatemi-J symmetric causality test detects a one-way causality from Bitcoin to the USD, while Hatemi-J asymmetric test reveals a unidirectional causality from positive Bitcoin shocks to negative shocks of BIST100 and Gold, and bidirectional causality with USD's negative shocks. Additionally, a bidirectional causality exists from Bitcoin's negative shocks to Gold's positive shocks and a unidirectional causality to USD's negative shocks. Recognizing Bitcoin as a financial asset sheds light on its interaction with traditional markets, aiding investors in refining strategies. In summary, this study enhances comprehension of cryptocurrency's role by emphasizing the causal link between Bitcoin and the USD.
We discover a novel flight-to-safety (FTS) effect from cryptocurrency markets to stock markets, triggered by a series of hacking attacks on cryptocurrency exchanges. This phenomenon is driven by heightened uncertainty, which increases investorsâ risk awareness and prompts asset reallocation in favour of safer stock markets over riskier cryptocurrency markets. We conduct an extensive global examination of this effect across 39 countries and confirm this novelty. This effect is amplified by frequent attacks when investorsâ risk awareness is strengthened. Notably, social media sentiment surrounding these attacks serves as both a timely warning indicator for upcoming hacking events and a measure of the FTS pressure following such attacks. We conclude that the collapsed investor confidence and increased risk aversion are the primary cause of such an effect. We further substantiate the FTS hypothesis by offering evidence of significant abnormal fund flows into US mutual funds following these hacking events. As such, through the lens of cyber attacks, we document how a shock in cryptocurrency markets is transmitted into stock markets via investorsâ FTS behaviour. ⢠We discover a flight-to-safety (FTS) effect from cryptocurrency to stock markets. ⢠The FTS effect is amplified by more frequent cyberattacks. ⢠Social media sentiment can warn upcoming hacking events and measure FTS pressure. ⢠The FTS is driven by collapsing investor confidence and heightened risk aversion. ⢠Evidence from US mutual fund supports our novel FTS effect.
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causalânoncausal invertibleânoninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMAâGARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewnessâcommon in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (Îą) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
Purpose Set against a rapidly evolving technologically driven investment landscape, this research aims to explore the complex interrelations among artificial intelligence, alternative energy stocks, eco-friendly investments, geopolitical risks (GPRs) and Ethereumâs energy consumption. Design/methodology/approach This work encompasses deploying the H2O Automated Machine Learning approach, explicitly focusing on analyzing market indicators. Additionally, the research emphasizes the evaluation of feature significance, identifying crucial variables that significantly influence the predictive outcomes. Besides, this study employs Shapley Additive Explanations to interpret the modelâs output, offering a detailed analysis of feature contributions and enhancing the modelâs transparency. Findings Key variables such as GPR, clean market (PBW) and the natural gas index (NG) significantly influence oil price predictions. The model demonstrates reliability, with areas for improvement in capturing unexplained variance. Practical implications This study offers valuable insights for energy sector market analysts, traders and policymakers, aiding in strategic decision-making and understanding market trends. Social implications This research emphasizes fostering clean and sustainable energy markets. It emphasizes the crucial role of advancements in artificial intelligence and renewable energy investments in accelerating the transition to environmentally responsible energy markets, highlighting their significance in fostering sustainability and mitigating climate change impacts. Originality/value This study pioneers integrating cutting-edge machine learning methodologies with crude oil market analysis, shedding light on critical influencing factors and forecasting aspects.
The aim of this paper is to forecast the volatility of Ethereum for a specified time period. To achieve this, we evaluated and compared the performance of five different models: Generalised Autoregressive Conditional Heteroscedasticity (GARCH), Long Short-Term Memory (LSTM), Random Walk Model (RWM), Neural Network Baseline Metrics - Fully Connected Network, and Baseline Model. After applying these models, we compared the estimated volatilities with the realized volatilities to assess the prediction accuracy. Considering the result comprehensively, the GARCH(1,1) is the most accurate model among these five. Our results revealed interesting insights into the nature of Ethereum, which behaves differently from traditional currencies. However, given Ethereum's early-stage behavior, future results may vary.
Abstract Since its introduction as a decentralized digital currency for peer-to-peer transactions, Bitcoinâs role in financial markets has undergone significant evolution. We employ bibliometric analysis to explore research trends in Bitcoin, identifying two primary perspectives in the recent financial economic literature: Bitcoin as a speculative asset and as a safe-haven asset. The speculative nature of Bitcoin is evident through its high volatility and frequent price jumps, largely influenced by rapid shifts in investor sentiment and attention, which create both risks and opportunities for traders. Conversely, Bitcoin exhibits characteristics of a safe-haven asset due to its asymmetric tail dependence and negative correlation within certain asset classes.
The cryptocurrency market, known for its inherent volatility, has been significantly influenced by external shocks, particularly during periods of global crises such as the COVID-19 pandemic and the RussiaâUkraine war. This study investigates the volatility of the top seven cryptocurrencies by market capitalizationâBitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), USD Coin (USDC), XRP, and Cardano (ADA)âfrom 1 January 2020 to 1 September 2024, employing a range of GARCH models (GARCH, EGARCH, TGARCH, and DCC-GARCH). This research aims to examine the persistence of leverage effects, volatility asymmetry, and the impact of past price fluctuations on future volatility, with a particular focus on how these dynamics were shaped by the pandemic and geopolitical tensions. The findings reveal that past price fluctuations had a limited impact on future volatility for most cryptocurrencies, although leverage effects became evident during market anomalies. Stablecoins (USDC and USDT) showed a distinct volatility pattern, reflecting their peg to the US Dollar, while platform-associated BNB demonstrated unique volatility characteristics. The results underscore the marketâs sensitivity to price movements, highlighting the varying reactions of investor profiles across different cryptocurrencies. These insights contribute to understanding volatility transmission within the cryptocurrency market during times of crisis and offer important implications for market participants, particularly in the context of risk management strategies.
Abstract During the last years, financial market contagion has become a critical concern for policymakers and investors, particularly with respect to the financial stability of cryptocurrency platforms. This paper explores the contagion effect among crypto exchanges employing the SusceptibleâInfectedâRecovered (SIR) model with time delay and investigates possible cooperative strategies. The SIR dynamical system is integrated with the replicator equation of evolutionary game theory to study the interplay between the spread of risk and the propensity of cryptocurrency platforms to become cooperative under the pressure of financial contagion. Different equilibrium points which correspond to both pure and mixed cooperative strategies characterize the resulting model. We carry out a theoretical analysis of the problem by studying the asymptotic behavior in the steady state. In addition, using extensive cryptocurrency market data from 2017 to 2023, we identify the key factors driving contagion and assess the dynamics of cooperative versus non-cooperative behavior. Our findings point out that cooperative strategies are essential to ensure financial stability, particularly in the long term, as they mitigate systemic risks and foster resilience. These results provide critical insights for policy makers and investors, offering actionable strategies to enhance the robustness of crypto markets and address the growing challenges of financial contagion in the digital asset ecosystem.
Abstract This paper examines the dependence, systemic risk spillover, return and volatility spillover, and portfolio implications across various timescales between the Green Bond (GB) and U.S. S&P 500 Stock (SP), Vanguard Total World Stock Index Fund (VT), Bitcoin (BTC), Ethereum (ETH), Ripple, OIL, and GOLD markets. The sample period is August 07, 2015âOctober 6, 2023, covering periods of instability during the COVID-19 pandemic and the RussiaâUkraine conflict. Using the waveletâcopulaâconditional value-at-risk and wavelet-multivariate asymmetric-GARCH framework, our main results show that the systemic risk and return, volatility spillovers, and diversification opportunities are portfolio-specific and timescale-dependent. Specifically, there is a negative long-term correlation for the pairs GB-SP and GB-OIL, whereas the pair GBâGOLD pair is positively correlated in the short term. GB can mitigate the risk of other markets. In terms of the portfolio implications, GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term. Regarding hedging effectiveness, the role of GB for GOLD and VT is noted.
Inzamam Ul Haq, Muhammad Abubakr Naeem, Chunhui Huo, Walid Bakry
This study examines the interlinkages among diverse cryptocurrency classes and their multiscale relationship with media climate change concerns to examine how cryptocurrency returns respond to rising climate change concerns. The analysis includes 11 cryptocurrencies classified as dirty, gold-backed, energy, and sustainable and their behavior regarding media climate change concerns, including transition and physical risks. Using squared wavelet coherence and partial wavelet coherence (PWC) on daily data from January 1, 2014 to June 29, 2024, this study shows time-frequency-dependent market integration among cryptocurrency pairs. During rising climate change concerns, returns decrease for some cryptocurrencies while increasing for XRP, implying higher investors' trust in sustainable cryptocurrencies. PWC analysis reveals significant influence of climate change concerns on pairwise returns connectedness among various cryptocurrency classes. This study highlights the need for cryptocurrency traders to incorporate media climate change information into their investment decisions, contributing insights into using diverse crypto-assets for risk management. ⢠We find high market integration after 2018 cryptocurrency crash. ⢠PLG and gold-backed cryptos show weak dependence with respective cryptocurrencies. ⢠Rising climate change concerns significantly increase PLG and XRP returns across time-frequency. ⢠We find that transition risks predict cryptocurrency returns more than physical risks. ⢠We find that climate change concerns drive cryptocurrency co-movements.
Adi Wolfson, Gerard Khaladjan, Yotam Lurie, Shlomo Mark
Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effectiveâand not just more efficientâsolutions, based on value co-creation between several providers and a customer.
We examine if the day-of-the-week effect is present in Bitcoin return series. The model specification in use accounts for conditional heteroscedasticity, which is captured in the form of a stochastic volatility process that allows for periodic time-varying parameters. We find periodicity in Bitcoin returns, which is evidence against the market efficiency of Bitcoin.
Sami Ben Jabeur, Zouhaier Dhifaoui, Yassine Bakkar, Houssein Ballouk
This study provides evidence on the role of the quality of political signals in predicting six major cryptocurrency asset classes. Including communications from the U.S. presidential election in 2024, we find that political news affects cryptocurrency returns in the short-term (from ⟠2 to ⟠4 months). For most cryptoassets, text sentiment measures demonstrate superior predictive performance compared to historical cryptocurrency time series in out-of-sample forecasts. ⢠Analyzes the effect of political signals on cryptocurrency returns. ⢠Political news influences cryptocurrency returns in the short term. ⢠Political signals enhances the accuracy of Bitcoin return forecasts.
Lingli Qing, Ibrahim Alnafrah, Abd Alwahed Dagestani
The energy-intensive nature of cryptocurrency mining, largely reliant on fossil fuels in its early development, has raised growing environmental concern. Consequently, the Index of Cryptocurrency Environmental Attention (ICEA) has emerged, gauging public attention towards this issue. This study investigates the complex interplay between ICEA, cryptocurrency price and policy volatilities, green energy investments, and dirty energy prices. Utilizing a dataset spanning from January 2015 to June 2023, we employ a multifaceted approach encompassing cross-quantilogram, time-varying parameter vector autoregression (TVP-VAR), and wavelet coherence techniques to uncover the dynamic interconnectedness of these three markets. Our findings challenge a simplistic narrative that anticipates a direct link between ICEA and immediate reductions in electricity consumption within the cryptocurrency mining sector. Instead, we discern a nuanced picture wherein ICEA drives significant structural transformations, influencing investments in clean energy markets. Our analysis suggests that ICEA stimulates green energy investments, encouraging miners to explore alternative energy sources with lower environmental impacts . This transition paves the way for more sustainable investments , with green energy sources like renewables playing an increasingly prominent role in powering the cryptocurrency industry .
This study investigates the return propagation dynamics between cryptocurrencies and Emerging market sectoral indices (EMSI), focusing on portfolio impact from Bitcoin, Ethereum, and two gold-backed cryptocurrencies (PAXG and X8X). Using data from 2019 to 2024, we apply a novel DCC-GARCH-based R 2 decomposed connectedness approach to analyse return connectedness among these high-risk assets. We also utilize innovative concepts such as minimum dynamic pairwise connectedness and minimum R 2 decomposed connectedness portfolios in our multivariate hedging portfolios. Our findings reveal that total connectedness is time-variant and influenced by economic events. Bitcoin and Ethereum are identified as net transmitters of shocks, while other assets, particularly gold-backed cryptocurrencies, serve as net shock receivers with minimal impact. Moreover, few EMSIs (financials, industrials, and materials sectors) show significant connectedness in the system. Although our suggested portfolio analysis offers improved returns, none consistently outperform the market. This research offers valuable insights for investors and policymakers regarding the interconnectedness and risk management of cryptocurrencies and EMSI.