This study evaluates the risk-adjusted consequences of large-weight equity–cryptocurrency substitution and examines whether these effects differ systematically across equity styles (Growth vs. Value) and regions (Asia, Europe, and the Americas). Using daily data from January 1, 2021 to December 31, 2023, the analysis constructs style-segmented MSCI country equity indices and compares annual Sharpe ratios under four constant-mix, daily rebalanced strategies: (S1) 100% equity; (S2) 50% equity / 50% cryptocurrency; (S3) 50% equity / 50% global bonds; and (S4) 33.33% equity / 33.33% global bonds / 33.33% cryptocurrency. Five major non-stablecoin cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Solana (SOL), and Binance Coin (BNB)—are evaluated individually to isolate coin-specific substitution effects. Performance is assessed annually and compared across Growth and Value portfolios within identical country–year–cryptocurrency environments to identify style-dependent outcomes. The results show that cryptocurrency substitution generally improves Sharpe ratios, but the effects are benchmark-, coin-, style-, and region-dependent. Improvements are more heterogeneous under direct 50% equity–crypto substitution, especially for Value portfolios, but become uniformly positive when crypto is introduced within an equity–bond benchmark. SOL provides the largest and most consistent improvements, while ETH and BNB are frequently strong and BTC is the least consistent. Growth portfolios benefit more than Value portfolios in Asia and the Americas, whereas Europe shows more style-neutral effects. Because cryptocurrency shocks are common within a given coin-year, statistical inference is interpreted as cross-market evidence rather than fully independent observations.
Abstract This study examines the dynamic volatility spillover between financial technologies and traditional and alternative financial markets. The analysis utilizes daily data covering the period from January 2018 to March 2026 for FinTech ETFs, the Nasdaq, Bitcoin, and gold markets. Interconnectivity among financial markets was analyzed using the time-varying parameter VAR (TVP-VAR) connectedness approach. The findings indicate the presence of a moderate and time-varying connectivity structure among the markets. In particular, a strong interaction was observed between the FinTech and Nasdaq markets, while Bitcoin was found to play a significant role as a volatility transmitter during certain periods. The gold market, on the other hand, generally exhibited more stable and limited interactions. Additionally, the study found that financial market linkages increase during periods of crisis and uncertainty. These results highlight that the transmission of volatility across financial markets has a dynamic structure and that portfolio diversification strategies should be evaluated accordingly. By examining FinTech markets alongside other major asset classes, the study provides a timely and comprehensive contribution to the literature.
Bitcoin has become an increasingly important asset for portfolio allocation, yet its diversification value and option-implied information remain difficult to evaluate. This paper examines Bitcoin risk from portfolio and option-implied perspectives. This study assesses whether Bitcoin improves the risk-return opportunity set with traditional assets and whether its diversification role remains stable during market stress. Option-implied measures, including the 25-delta Risk Reversal (RR25), smile curvature, and an at-the-money Implied-Volatility-minus-Realized-Volatility (IV-minus-RV) proxy, are then constructed to predict market conditions. Portfolio analysis shows that Bitcoin can improve risk-return tradeoffs but does not function as a stable minimum-variance asset or a reliable crisis hedge. Baseline regressions provide limited evidence that RR25 consistently predicts future realized volatility or returns. However, extreme negative short-dated RR25 is followed by higher future realized volatility, suggesting RR25 is more informative as a nonlinear stress-state indicator than as a continuous forecasting variable. Smile curvature captures the implied-volatility surface but provides weaker predictive information. Finally, the IV-minus-RV analysis shows that gradual RR25-based exposure scaling achieves a better risk-adjusted profile than a binary exposure rule. Overall, the findings indicate that Bitcoin's diversification benefits and option-implied information are state-dependent.
Abstract The emergence of cryptocurrencies has presented investors with novel portfolio diversification opportunities. This study investigates the interplay between precious metals and cryptocurrencies, examining their potential for enhancing portfolio returns and mitigating risk. Using daily closing prices from August 2017 to November 2022, we employ an autoregressive distributed lag (ARDL) approach to analyze comovement and causality between these asset classes. Our findings reveal a short-run linkage between Bitcoin, precious metals, and other cryptocurrencies but no long-run cointegration. Notably, gold prices unidirectionally influence cryptocurrency prices, a relationship not observed with other assets. This absence of long-term comovement suggests that precious metal investors can leverage modern portfolio theory to diversify cryptocurrency volatility risk. These results offer valuable insights for investors seeking to optimize portfolios by strategically incorporating cryptocurrencies for improved risk-adjusted returns.
This study proposes a Bitcoin price prediction model utilizing Long Short-Term Memory (LSTM) networks, integrating technical indicators, Reddit sentiment indicators, and on-chain data. The cryptocurrency market, particularly Bitcoin, exhibits extreme price volatility, despite its high profit potential. This volatility stems from a combination of macroeconomic factors, market participant sentiment, and fluctuations in supply and demand within the blockchain ecosystem. Existing literature typically examines only one or two types of data—whether technical, sentiment, or on-chain—without systematically verifying the complementary effects of integrating these heterogeneous data sources on predictive performance. To address this gap, this study quantitatively analyzes the contribution of each data type by constructing four experimental settings combining technical indicators, Reddit sentiment, and on-chain data based on Bitcoin price movements. Moreover, the proposed LSTM model is benchmarked against Linear Regression (LR), Random Forest (RF), and XGBoost (XGB) under identical experimental conditions. Evaluation metrics, including RMSE, MAE, and MAPE, indicate that while the LSTM model demonstrates superior predictive performance using only technical indicators, the inclusion of both Reddit sentiment and on-chain indicators results in a slight increase in error metrics. Nevertheless, this study emphasizes the potential of capturing the multifaceted characteristics of the market, which are often overlooked with single price-based indicators. It provides an empirical foundation supporting the effectiveness of heterogeneous data integration in future cryptocurrency price prediction research.
This study proposes a framework combining Variational Mode Decomposition (VMD) with a relevance-driven selection process to reduce noise and redundancy in financial time-series forecasting. The original time series is decomposed by VMD into intrinsic mode functions (IMFs), which are then evaluated using three relevance metrics: relative energy contribution, mutual information, and Spearman's rank correlation coefficient. These metrics identify the IMFs most strongly associated with future price movements. As opposed to conventional VMD-based approaches that treat all IMFs equally, the proposed relevance-driven selection process adapts IMF selection to the statistical properties of the analyzed market, thereby improving model generalization across different volatility conditions and forecasting horizons. This study makes three main contributions: (i) developing a relevance-driven IMF selection strategy to overcome limitations of traditional VMD methods, (ii) designing a hybrid framework that integrates multiscale decomposition with nonlinear information filtering, and (iii) conducting a comprehensive empirical evaluation of the proposed models. Experiments on hourly Bitcoin (BTC)/USD data from 2018 to 2025 show that the VMD-RDIC-deep learning models achieves strong forecasting performance. The results show that the proposed relevance-driven decomposition framework improves prediction accuracy and robustness compared with traditional statistical models, including Autoregressive Integrated Moving Average (ARIMA), as well as machine learning and deep learning approaches, highlighting its suitability for complex and volatile financial markets. Received: 18 January 2026 | Revised: 13 April 2026 | Accepted: 23 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://www.kaggle.com/datasets/novandraanugrah/bitcoin-historical-datasets-2018-2024. Author Contribution Statement Maryam Maatallah: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Visualization. Mourad Fariss: Software, Formal analysis, Writing – original draft. Hakima Asaidi: Validation, Investigation, Writing – review & editing. Mohamed Bellouki: Resources, Writing – review & editing, Supervision, Project administration.
The rapid expansion of the digital currency market and the growing role of stablecoins as potential intermediaries have brought its interconnectedness with traditional financial markets to the forefront of global financial research. Using daily data from 4 January 2021 to 30 September 2025, this study constructs a variable system with the price indices of USDT and USDC as core digital currency proxies, alongside traditional financial asset indices for stocks, bonds, and gold derived via the entropy weight method. We employ a comprehensive set of econometric techniques, including static correlation analysis, vector autoregression (VAR), impulse response functions, and extreme-event shock tests, to systematically investigate the interdependence structure, risk spillover dynamics, time-varying co-movements, and structural changes between the two markets during extreme risk episodes. The findings reveal an overall weak and asymmetric bidirectional spillover relationship between the cryptocurrency and traditional financial markets. Volatility in the digital currency market is found to be largely endogenous, with a limited capacity to transmit shocks externally. Conversely, traditional financial markets—particularly the equity market—exert a more pronounced influence on the digital currency market. Critically, under the impact of extreme risk events, the cross-market linkages exhibit structural breaks; the direction and intensity of correlation can strengthen significantly or even reverse, demonstrating a clear state-dependency. This research provides empirical evidence for understanding the functional role of digital assets within the macro-financial system, their risk transmission pathways, and their implications for systemic financial stability. The findings offer valuable theoretical and practical insights for financial regulators in designing robust cross-market risk prevention frameworks and for investors seeking to optimize asset allocation strategies.
Arif Billah Dar, Isha Kumari, Safika Praveen Sheikh
Abstract The financialization of gold is investigated by analyzing the dynamic return spillovers between traditional, equity-based, exchange-traded and blockchain-based gold investment means. The empirical system includes physical gold, the SPDR Gold Shares ETF (GLD), VanEck Gold Miners ETF (GDX), NYSE Arca Gold BUGS Index (HUI), First Trust Gold Miners ETF (FTGM), PAX Gold (PAXG), and Tether Gold (XAUT). The study employs the Time-Varying Parameter Vector Autoregressive connectedness framework with generalized forecast error variance decomposition to estimate the magnitude, direction and evolution of shock transmission. The findings show that the financial economy around gold is quite tightly connected, with an average TCI of 81.13%, meaning that most of the forecast error variance of the system is attributable to cross-market shocks. GLD is the biggest net shock transmitter followed by GDX and XAUT, while the biggest net receivers are physical gold and PAXG. The results indicate that the price discovery and information transmission have been shifting from the underlying physical bullion market to exchange-traded markets and digital gold markets. The dynamic analysis also reveals that connectedness remains high but fluctuates throughout the sample, whereas PAXG gradually evolves from a net receiver to a net transmitter in the later part of the sample. Using the alternate Quantile connectedness methodology, we also find that the results are robust. Overall, it is clear that the results reflect the gradual transformation of gold from a commodity asset to a highly networked financial asset. The results are relevant to portfolio diversification, hedging, market monitoring and regulation of new gold products.
Background. Technical analysis remains among the most accessible forms of market decision support, but its incremental predictive value depends on design choices, and repeated model selection can create spurious backtest performance. Objective. This study evaluates whether widely used technical indicators, reconstructed from public formulas, can predict the next daily price direction of Bitcoin, the S&P 500, and gold. Methods. Daily data from 1 January 2015 to 14 July 2026 are examined using a chronological walk-forward design. Every signal observed at the close of day t is matched only with the sign of the close-to-close return from t to t+1. The main out-of-sample period begins in 2019, with 2019–2022 used for model selection and 2023–2026 reserved for confirmation. Performance is measured primarily by balanced accuracy and supplemented by accuracy, directional recall, stationary-bootstrap confidence intervals, and after-cost trading outcomes. Results. The best single indicator, Ichimoku 9/26/52, produced a macro balanced accuracy of 50.9%, while the best predetermined combination, Volume Confirmed, reached 50.8%. A new ridge-logistic hybrid indicator, SPAH-1, achieved 51.9% in validation and 51.3% in confirmation. Its confirmation balanced accuracy was 49.8% for Bitcoin, 49.2% for the S&P 500 proxy, and 55.0% for gold; only gold's bootstrap interval excluded 50%, but its predictions were strongly biased toward the upward class. After trading costs, Volume Confirmed underperformed buy-and-hold for all three assets. Additional selective experiments did not support an 80% daily prediction target. Conclusion. Technical indicators may assist regime description and decision confirmation, but they do not provide a robust universal next-day forecasting edge.
Harini D., S. Aruna, Santhanalakshmi V., D. P. Sivasakti Balan · 5 authors
Cryptocurrencies have emerged as a significant component of the global financial systems, attracting considerable attentions from investors, researchers and policymakers. Simultaneously, Indian sectoral stock market plays a crucial role in the country’s economic development and investment landscape. Due to their high volatility, crypto currency markets may influence traditional financial markets and investment decisions .This study investigates the impact of crypto currency market movements from on selected Indian sectoral stock markets, including the information Technology, Banking, Pharmaceutical sectors. Historical data from major cryptocurrencies and sectoral stock indices like Bitcoin, Ethereum, Nifty IT, Nifty Bank, and Nifty Pharma indices which was collected and analyzed using explainable regression techniques. Based on the given datasets, the findings provide valuable insights into the relationship between cryptocurrency markets and selected Indian sectoral stock market indices, particularly in understanding how crypto currency market fluctuations during major global events, such as pandemics, geopolitical conflicts, and economic uncertainty, may influence investor behavior and sectoral performance across the Indian economy.
This study examines whether major cryptocurrency returns respond systematically to scheduled Federal Reserve (Fed) interest rate announcements and whether FOMC-window movements are explained more by realised policy decisions or by broader risk-sentiment conditions. Using daily data for Bitcoin, Ethereum, XRP, Dogecoin, Solana, the U.S. Dollar Index, and VIX, the analysis covers 43 scheduled FOMC announcements between 2021 and 2026. Six cumulative event-window returns are evaluated through parametric mean tests, Wilcoxon signed-rank tests, and panel event-study regressions with crypto fixed effects and FOMC-event-clustered standard errors. Because the available surprise measure contains only two nonzero observations, the study focuses on realised rate changes, hike/cut/hold categories, asymmetric rate-change magnitudes, VIX changes, and DXY returns rather than formal monetary policy shocks. The results provide little evidence that cryptocurrency returns differ systematically from zero around FOMC announcements. Actual rate changes, policy-direction categories, and asymmetric hike/cut magnitudes do not robustly explain event-window returns, and crypto-specific interaction models provide no stable evidence of heterogeneous sensitivity across assets. By contrast, VIX changes are negatively and significantly associated with cryptocurrency returns in several windows, while DXY effects are weak and unstable. The study contributes by showing that FOMC-window cryptocurrency performance is better explained by risk-sentiment conditions than by the realised size or direction of Fed rate decisions.
Pham Ngoc Toan, Le Tran Trung Hieu, Nguyen Vu Trung Nguyen
Carbon pricing is jurisdictional, while proof-of-work cryptocurrency mining is a highly mobile electricity load. We examine whether daily power-sector emissions display a cross-regional and distributional pattern consistent with short-run emissions displacement. Using daily observations covering calendar years 2019–2025 (with a boundary observation on 1 January 2026; N = 2550 after transformation and cleaning), we estimate quantile regressions for the EU27, the Russian Federation and the rest of the world using the interaction between Bitcoin returns and European carbon-allowance returns. The focal Russian lower-tail interaction is positive (q10 beta = 0.0662); OLS and dynamic specifications remain positive, and a 1000-replication pairs bootstrap gives p = 0.0077. The association survives a trading-day-only sample, calendar and persistence controls, and a seven-lag specification, while randomised-carbon and non-power-sector placebo outcomes are null. However, the coefficient loses conventional significance without Winsorisation, the May-2021 Chinese-ban timing prediction is not supported, and a direct EU27-minus-Russia substitution diagnostic is null. Quantile-on-quantile estimates place the largest Russian Bitcoin-return coefficients in high-carbon-price, low-emission states, but remain descriptive. Because the design does not observe mining capacity moving across jurisdictions and the available full-sample Russian emissions series is national rather than subnational, the evidence supports a leakage-consistent operational association rather than proof of physical relocation or a broad causal effect of EU carbon pricing.
This study examines the effect of blockchain adoption on market efficiency in selected African capital markets from 2014 to 2025. It is motivated by persistent inefficiencies in African stock exchanges, including weak liquidity, information asymmetry, delayed settlement, high transaction costs, and limited digital financial infrastructure. The study adopts a quantitative longitudinal panel design and develops a Blockchain Adoption Index covering blockchain infrastructure, settlement digitisation, fintech ecosystem indicators, and regulatory innovation. Market efficiency is measured using stock return predictability, bid-ask spread, price delay, turnover ratio, and information efficiency indicators, while institutional quality is introduced as a moderating variable. The study applies Dynamic Panel System Generalised Method of Moments estimation to address endogeneity, persistence effects, and unobserved heterogeneity. The findings show that blockchain adoption has a positive and statistically significant effect on market efficiency across African capital markets. Specifically, blockchain adoption improves liquidity, reduces informational frictions, narrows bid-ask spreads, and strengthens price discovery. The interaction result further shows that institutional quality enhances the positive effect of blockchain adoption on market efficiency. The study concludes that blockchain-enabled financial infrastructure can improve capital market performance in Africa when supported by strong governance, credible regulation, and effective digital infrastructure. It recommends increased investment in exchange digitisation, blockchain-based settlement systems, regulatory harmonisation, and institutional capacity development.
Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring.
Muhammad Diaz Syahmi Oktavian, Rizky Parlika, Firza Prima Aditiawan
The extreme price volatility of Bitcoin frequently prevents its widespread adoption. The persistent "Digital Gold" narrative often dominates its price analysis, largely ignoring the predictive value of strategic industrial commodities like Platinum Group Metals. This study aims to investigate whether integrating industrial metals specifically platinum and rhodium enhances the short-term forecasting accuracy of Bitcoin prices. Utilizing high-frequency 5-minute interval data over 729 days, this research applies a comparative quantitative approach using univariate and multivariate Long Short-Term Memory (LSTM) deep learning architectures. Results demonstrate the multivariate LSTM model achieves highly accurate forecasting, recording a Mean Absolute Percentage Error (MAPE) of 3.95% and a Root Mean Squared Error (RMSE) of 0.0598. Compared to the univariate baseline model (MAPE of 5.14%, RMSE of 0.0725), the multivariate approach demonstrates a notable decrease in error rates. This improvement suggests platinum and rhodium price movements contain useful informational value for Bitcoin forecasting, rather than mere random noise. Specifically, rhodium demonstrates strong predictive relevance for Bitcoin market movements. In conclusion, while not strictly proving causal structural integration, these findings highlight Bitcoin's sensitivity to the global real-sector economic cycle. Practically, these findings suggest investors can refine short-horizon forecasting and mitigate risk by monitoring industrial commodity prices. Given persistent nominal offset deviations, future research should prioritize explicit connectedness testing (e.g., lead-lag analysis) and develop a hybrid model incorporating Natural Language Processing (NLP) for news sentiment analysis.
This study examines the short-run effects of U.S. monetary policy shocks on cryptocurrency returns and asks whether digital assets respond to conventional macroeconomic transmission mechanisms. Focusing on the post-2020 period, it evaluates the magnitude, direction, and persistence of Federal Reserve rate shocks across Bitcoin, Ethereum, Solana, Ripple, and TRON. The analysis applies an SVAR-X framework to daily data for January 2020-December 2025. Cryptocurrency log returns are treated as endogenous variables, while the U.S. Dollar Index and VIX are included as exogenous controls; federal funds rate changes are modelled as strictly exogenous policy shocks. Impulse-response results show positive and significant contemporaneous responses for Bitcoin, Ethereum, Solana, and TRON, but no significant reaction for XRP. These effects dissipate within days, indicating modest, short-lived, and heterogeneous monetary-policy transmission rather than persistent effects on cryptocurrency return dynamics over time.
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.
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.
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.