Uncertainty plays a significant role in shaping investment decisions, both directly and indirectly. In an uncertain economic environment, investors’ motivations for decision-making may vary. While some investors tend to seek safe-haven assets, others may engage in speculative behavior. Therefore, uncertainty can influence financial instruments through various mechanisms. One of these instruments is Bitcoin, which is often regarded as the “gold” of cryptocurrencies. Compared to traditional financial investment instruments, Bitcoin exhibits higher volatility and is among the primary assets that may be affected by uncertainty. However, an important question is whether this effect is temporary or permanent. The main objective of this study is to address this question by examining the causal nexus between Global Economic Policy Uncertainty (GEPU) and Bitcoin by employing a frequency-domain causality approach. In this context, the causal relationships between GEPU and BTC prices are examined for the entire period and for different sub-periods. Although the study's findings show no causal relationship between the variables over the entire period, the analyses for the short-, medium-, and long-run indicate a causal relationship from GEPU to BTC in the medium run. Accordingly, GEPU can be considered one of the factors affecting BTC price; however, its impact does not appear to be persistent.
Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape.
High-Frequency Foreign Exchange (FX) electronic execution networks process in excess of $7.5 trillion in daily spot volume across geographically distributed matching engines. Modern institutional trading infrastructure relies heavily on automated limit order book (LOB) forecasting and real-time natural language processing of macroeconomic news feeds. However, this convergence of deep learning and automated execution introduces systemic attack surfaces that traditional risk engines are unequipped to handle. In this paper, we present FOREX-SHIELD, an integrated, multi-modal cyber-defense pipeline engineered to mitigate spoofing, news injection, and regulatory privacy leaks in high-frequency FX settlement. First, we model high-frequency 40 x N LOB dynamics using a spatio-temporal DeepLOB framework combining 2D convolutional layers and recurrent units. We demonstrate that unhardened spatial price-volume representations are vulnerable to microsecond Targeted Projected Gradient Descent (PGD) perturbations, suffering an Attack Success Rate (ASR) up to 37.50% (and 15.62% under expanded 64-sample batch evaluations). To counter this, we implement dynamic on-the-fly adversarial retraining, which elevates model defense robustness up to 84.38%–100.00% (preventing 54 out of 64 prediction flips). Second, we fine-tune a domain-adapted financial Transformer (FinBERT) using class-weighted cross-entropy optimization to detect synthetic macro news attacks, achieving 85.71% accuracy, an F1-score of 85.71%, and 100.00% recall across adversarial probes. Third, we construct a Zero-Knowledge Proof (ZK-SNARK) settlement layer that deterministically validates Anti-Money Laundering (AML) risk limits and liquidity constraints (R <= 75) without leaking transaction balances or institutional counterparty metadata. End-to-end backtests show a full multi-stage evaluation latency of approximately 120 ms, proving operational viability for real-time institutional clearing.
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.
Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors
This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.
본 연구는 비트코인과 동북아시아 주식시장(한국, 중국, 일본) 간 동태적 상호의존성을 분석한다. 이를 위해 VAR 모형의 충격반응함수와 Diebold and Yilmaz(2009)가 제안한 전이효과 모형을 이용하여 금융시장 간 파급효과를 측정했다. 또한 코로나19 팬데믹의 영향을 분석하기 위해 분석 기간을 코로나19 이전, 코로나19 기간, 코로나19 이후로 구분했다.<br/> 주요분석 결과는 다음과 같다. 첫째, 코로나19 이전 기간에는 비트코인과 주식시장 간 연관관계가 제한적인 것으로 나타났다. 둘째, 코로나19 기간에는 금융시장 불확실성 확대와 글로벌 유동성 증가로 인해 비트코인이 주식시장에 미치는 전이효과가 크게 확대됐다. 셋째, 코로나19 이후 기간에는 연관관계가 코로나19 기간보다는 완화됐으나 코로나19 이전 기간보다는 높은 수준을 유지했다. 이는 점진적으로 비트코인이 주식시장과 통합되고 있음을 시사한다. 한편, 중국 주식시장의 경우 가상화폐 규제로 인해 비트코인의 파급효과가 상대적으로 제한적으로 나타났다.
Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and reference guide for Bitcoin on-chain time series designed for economic and econometric research. The dataset provides documented daily series covering block production, block-space usage, transaction counts, supply, issuance, fees, miner revenue, mining difficulty, estimated hashrate, Bitcoin Days Destroyed, dormancy, liveliness, UTXO counts, spent output value, and related UTXO-age indicators. Metrics are reconstructed from a locally maintained Bitcoin Core full node, a persistent spent-output indexer, or deterministic transformations of previously generated OBM series. Each series is accompanied by open-source Python code, stable identifiers, explicit definitions, metadata, validation procedures, interpretive caveats, and comparisons with the closest publicly available metrics. The dataset is intended to support transparent empirical research, replication, teaching, and comparative analysis across monetary economics, financial economics, and blockchain studies.
Purpose This study investigates the volatility spillover dynamics between carbon credit market represented by European Union Allowance (EUA) futures and major cryptocurrencies, Bitcoin (BTC) and Ethereum (ETH), during the 2020–2024 period. It aims to understand whether these assets, despite their difference in regulatory and structural features, exhibit interconnected volatility pattern and particularly under crisis or shock conditions. Design/methodology/approach The article employs a two-step econometric approach. First, the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is used to estimate time-varying return correlations among EUA, BTC and ETH. And second, the Diebold–Yilmaz (2012) spillovers index based on forecast error variance decomposition is applied to quantify the sizes, directions and evolution of volatility spillovers across markets. Findings The results reveal significant but uneven and time-varying volatility spillovers between carbon and cryptocurrency markets. Spillover intensity becomes more prominent, especially during major crisis periods such as the COVID-19 pandemic, the Russia–Ukraine war and the FTX collapse. Spillovers are asymmetric and regime-dependent. ETH emerges as the main net volatility transmitter, while BTC exhibits a near-neutral and regime-dependent role, alternating between transmitting and receiving shocks. EUA futures remain largely insulated, with only limited outward volatility transmission even under extreme market conditions. These findings suggest the presence of conditional and crisis-driven spillover linkages between green and digital assets. Originality/value This is among the first studies to empirically examine the volatility transmissions between carbon credit and cryptocurrency market using advanced econometric tools. It contributes to the emerging green -digital finance literature by identifying dynamic and directional interdependency across these evolving asset types.
The paper aimed to investigate the statistical relationship between Bitcoin prices and Ethereum trading volumes, as well as to create a simple predictive model for Ethereum trading volumes based on Bitcoin prices. To perform Spearman’s rank correlation analysis and to construct an artificial neural network (ANN) model, daily closing prices of Bitcoin in USD and daily trading volumes of Ethereum were utilized. The timeframe covered by the data starts May 1, 2020 and ends November 22, 2025. In this study, Ethereum volumes were treated as the dependent variable, while Bitcoin prices served as the independent variable. The findings indicate a significant, moderate, positive correlation between Bitcoin prices and Ethereum volumes, and the ANN model successfully predicted Ethereum volumes with a high level of accuracy. These results reinforce existing evidence regarding the relationships among cryptocurrencies. Furthermore, by confirming the efficacy of artificial neural networks (ANN) in predicting trends within the cryptocurrency market, the study also makes a methodological contribution. In addition, the study also offers a simpler modelling approach that highlights the significance of bilateral interactions among major cryptocurrencies through a single-input model. Based on the impressive performance of the ANN model, exchanges, fintech companies, and investment firms could incorporate lightweight machine-learning systems into their forecasting tools to provide real-time analytics with minimal processing requirements.
Walid Mensi, Rim El Khoury, Abdullah AlGhazali, S K Kang
Abstract The increasing integration of green cryptocurrencies into financial markets raises critical questions about their effectiveness as diversification and hedging instruments. This study examines their role relative to traditional green assets, including the S&P Green Bond Index, S&P Global Clean Energy Index, and S&P ESG Leaders Index, via quantile vector autoregression (QVAR) over the period November 2017–July 2024. The results reveal a U-shaped connectedness pattern, where spillovers between green assets intensify under extreme market conditions, diminishing their diversification benefits. Green cryptocurrencies, particularly Cardano (ADA) and Stellar (XLM), function as primary transmitters of volatility, especially during extreme market conditions. Conversely, green assets, traditionally perceived as low risk, act as net receivers of volatility, failing to provide consistent downside protection and challenging their reliability in risk mitigation. Hedging analysis demonstrates limited risk mitigation from traditional green assets, with certain cryptocurrencies, such as NANO, providing superior hedging potential. These findings have important implications for investors and policymakers. Investors should reassess their reliance on traditional green assets for risk management and consider adaptive hedging strategies incorporating green cryptocurrencies. Regulators must address systemic risks associated with the growing influence of clean cryptocurrencies by implementing volatility thresholds and transparency measures. Future research should examine the regulatory impact and the evolving role of green financial instruments in sustainable portfolio management.
This study addresses the growing importance of cryptocurrency (CC) as a financial asset and its increasing popularity as an investment option. Given the rapid expansion of research in this field, the main objective is to systematically synthesize the existing literature on cryptocurrency investment and decision-making, focusing on its intellectual structure, dominant themes, theoretical foundations, and emerging research trends. To achieve this, a hybrid review framework is employed, combining a theory-based systematic literature review with bibliometric analysis, following PRISMA guidelines. The analysis covers 1,184 articles indexed in Scopus and published between 2015 and 2025. Additionally, the study integrates the TCCMR, ADO, and PICO frameworks to provide a comprehensive, multidimensional evaluation of the selected body of literature. The findings reveal that cryptocurrency research is predominantly focused on volatility, market connectedness, portfolio diversification, and behavioral aspects of investment. The results also indicate a strong reliance on econometric and predictive modeling approaches. Emerging research directions highlight increasing attention to sustainability concerns, regulatory challenges, and the application of artificial intelligence in investment analytics. Based on these insights, the study proposes a future research agenda emphasizing theoretical integration, methodological diversification, sustainability perspectives, and decision-oriented modeling. The implications of the research are relevant for investors, regulators, and financial institutions, as they provide a deeper understanding of risks, governance, and decision-making processes in cryptocurrency markets. This study contributes to the literature by offering a comprehensive knowledge structure and research roadmap, representing one of the first attempts to combine bibliometric mapping with TCCMR, ADO, and PICO frameworks in the context of cryptocurrency investment research.
The paper examines the volatility spillover effects and long-term relationship between cryptocurrencies and traditional financial markets in Türkiye using BEKK-GARCH and DCC-GARCH models. It analyses the perception of crypto assets as a “digital safe haven” in an economy marked by high inflation, exchange rate fragility, and financial uncertainty. Using monthly price data for Bitcoin, Ethereum, BIST-100, and Republic Gold from January 2010 to February 2025, the study applies unit root tests, Johansen cointegration, ARDL bounds, and Engle-Granger tests. Results show no long-term price cointegration, but Bitcoin and Ethereum returns are strongly correlated, with DCC-GARCH results showing a dynamic correlation above 50%, while gold and BIST-100 correlate weakly or negatively. BEKK-GARCH highlights significant volatility transmission from Bitcoin to Ethereum, with BIST-100 maintaining persistent volatility. The study concludes that crypto and traditional markets in Türkiye are not integrated long-term, but short-term interactions exist at the return level, with implications for portfolio diversification and financial stability.
This paper investigates the impact of uncertainty on investor overconfidence in the Bitcoin market. While prior studies mainly focus on returns and volatility, limited attention has been paid to behavioral responses. Using a nonlinear autoregressive distributed lag (NARDL) model and monthly data from June 2011 to August 2022, we examine the asymmetric effects of major U.S. uncertainty indices (EPU, GPR, CPU, TEU and EURQ). The results reveal significant asymmetries. In the short run, increases in EPU and GPR reduce investor overconfidence, while decreases have the opposite effect. TEU and EURQ negatively affect investor confidence in both the short and long run. These findings highlight the key role of information-based uncertainty in shaping investor behavior and contribute to the behavioral finance literature by providing new evidence from cryptocurrency markets.
An analysis of the ethical and intergenerational dimensions of contemporary energy-finance transitions by systematically mapping the scholarly intersection between crude oil price volatility and cryptocurrency markets is conducted in this study. Drawing on a comprehensive bibliometric and topic-modelling analysis of 4,147 Scopus-indexed publications published between 2014 and 2024, the research investigates how emerging digital financial systems interact with oil market instability and broader sustainability concerns. By integrating Latent Dirichlet Allocation topic modelling with co-citation and keyword network analysis, the study reveals evolving research themes related to energy financialization, decentralized finance, environmental externalities, and regulatory uncertainty. Beyond its technical contributions, the findings highlight critical ethical questions surrounding climate responsibility, distributive justice, and intergenerational equity, particularly in relation to energy-intensive cryptocurrency mining and speculative responses to oil price shocks. The paper advances the concept of moral imagination by demonstrating how financial and technological innovation can either reinforce unsustainable trajectories or support ethically grounded sustainability transitions. The results offer policy-relevant insights for regulators, investors, and institutions seeking to balance economic resilience with long-term environmental responsibility and justice for future generations.
Ahmad Yani, Septiana Sihombing, Yogi Cahyo Ginanjar
Bitcoin has emerged as a prominent digital asset that blends financial innovation, technological advancement, and speculative behavior. However, its growing adoption raises sustainability concerns due to energy-intensive mining and environmental impacts. This study investigates the determinants of Bitcoin prices within the framework of sustainable digital finance by integrating blockchain fundamentals, technical indicators, and macroeconomic variables. Using daily data from 24 November 2021 to 21 November 2024 (753 observations), the analysis conducted with Stata 16—examines miners’ revenue, hashrate, transactions per block, unique addresses, mining difficulty, and trade volume as internal factors, along with gold prices, WTI crude oil, and the S&P 500 index as external factors. Results show that miners’ revenue, hashrate, and transactions per block have positive and significant effects on Bitcoin prices, emphasizing the importance of mining performance and network activity. Trade volume and unique addresses also display positive but less consistent influences, while mining difficulty remains statistically insignificant. Among external factors, WTI crude oil significantly affects Bitcoin prices. Overall, findings suggest that Bitcoin operates as both a financial asset and a technology-driven ecosystem shaped by blockchain dynamics and macroeconomic conditions. The study highlights the need for sustainable mining practices and transparent regulatory frameworks to enhance environmental efficiency.
This study investigates the dynamic relationship between network activity and transaction fees in the Ethereum blockchain by analysing the interaction between Gas Used and Gas Price through a multivariate time series model. The objective is to determine whether variations in network demand influence short-term gas price fluctuations. Daily data of Gas Used and Gas Price were transformed into different logarithmic forms to ensure stationarity. The Augmented Dickey–Fuller test confirmed that both variables are stationary at the five percent significance level, with ADF statistics of −6.21 for Δlog (Gas Used) and −7.12 for Δlog (Gas Price), and p-values below 0.001. The Vector Autoregression model was estimated with an optimal lag length of fourteen days, selected using the Akaike Information Criterion, reflecting the persistence of network and fee dynamics. The results of the Granger causality test indicate a unidirectional causal relationship from Gas Used to Gas Price, with an F-statistic of 3.72 and a p-value of 0.018, suggesting that fluctuations in network demand significantly precede changes in gas pricing. The reverse direction is not significant, with an F-statistic of 1.26 and a p-value of 0.28, indicating that transaction fees do not predict network activity. The impulse response analysis shows that a one standard deviation shock in Gas Used increases Gas Price for two to three days before returning to equilibrium, while shocks in Gas Price have minimal effects on Gas Used. These findings confirm that Ethereum’s fee market operates primarily as a demand-driven mechanism were congestion and transaction volume shape short-term gas price movements.
This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.
This research examines the determinants of Bitcoin (BTC) valuation from January 2011 to December 2025 using Autoregressive Distributed Lag (ARDL) models. The empirical evidence supports the hypothesis that the monetary policy of the United States Federal Reserve—specifically liquidity expansion and interest rate adjustments—drives price dynamics, confirming a pro-cyclical nexus. At the microeconomic level, the density of active institutional addresses and the marginal cost of production significantly influence price trajectories. Furthermore, heightened market volatility, represented by the VIX, exerts a statistically significant negative impact on BTC returns. The findings suggest that Bitcoin has transitioned into a sophisticated value asset, underpinned by production efficiencies and an expanding institutional base. Consequently, Bitcoin represents a viable alternative to centralised financial systems, offering a potential hedge against inflation and the erosion of purchasing power. The study concludes that digital assets warrant inclusion within conservative institutional portfolios, notwithstanding the inherent speculative nature of the market.
Daniel González Cortés, Monomita Nandy, Suman Lodh
Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.
The article examines digital financial assets as an instrument of investment portfolio diversification in the Ukrainian investment context. The study argues that Bitcoin and Ethereum should not be assessed through general statements about financial innovation, but through their measurable contribution to portfolio return, volatility and risk-adjusted performance. The empirical part is based on an annual scenario model for 2020–2025 and compares portfolios with 0%, 1%, 3%, 5% and 10% exposure to BTC and ETH. The benchmark portfolio includes domestic government bonds, the USD/UAH currency component, gold and the S&P 500 as a global equity benchmark, while the local Ukrainian equity segment is interpreted cautiously because of its limited liquidity. The results show that portfolios with 1–5% exposure to BTC and ETH improved risk-adjusted efficiency compared with the baseline portfolio. The P3 scenario provided the most balanced relationship between return growth and risk growth, while P5 generated a higher average annual return with still acceptable volatility. The P10 scenario produced the highest geometric average annual return but almost tripled volatility compared with the baseline portfolio, making it more suitable for an aggressive investor profile. The article concludes that digital financial assets may have practical value only as a limited high-risk addition to a diversified portfolio, not as a stable hedging instrument or a substitute for traditional instruments.
As sustainability plays an increasingly important role in finance, understanding its influence on emerging assets such as cryptocurrencies is essential for portfolio management. This article analyzes the relation between sustainability and cryptocurrency returns. To address and reveal complexity in relationships, we use non-linear machine learning methods. We find that sustainability variables, like energy consumption and environmental attention, are important return determinants. The greenness of a cryptocurrency measured by the consensus mechanism is a group-specific differentiation variable for the most sustainable cryptocurrencies with a positive impact on their returns. The economic relevance of their green consensus mechanism materializes primarily in the lower tail of the return distribution by providing downside protection. We detect a clear upward trend in complexity, with maxima during COVID-19 and the change of Ethereum’s consensus mechanism from Proof of Work to the more environmentally friendly alternative Proof of Stake. These findings underline the importance of considering sustainability factors in cryptocurrency investment decisions, offering new insights for investors as well as policymakers.