Blockchain Papers

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Hongru He, Akihiro Fujihara

High-performance Byzantine Fault Tolerant (BFT) blockchains are designed to achieve high throughput and low latency, yet their observed block time distributions often reveal complex behaviors arising from networking, pipelining, and deployment heterogeneity. In this paper, we diagnose HotStuff-based high-performance BFT consensus by modeling block times through a quorum-based multicast framework that links each block interval to quorum formation latency. We capture multimodal block time distributions using mixture models, where each component represents a distinct network condition characterized by effective transfer rate of block information. The proposed model is fitted to the bulk of mainnet block time data, while tail decay is analyzed separately to assess asymptotic behavior. Applying this methodology to Hyperliquid and Aptos mainnets, we find that Hyperliquid is well explained by a unimodal distribution, consistent with a relatively homogeneous validator deployment. In contrast, Aptos exhibits persistent multimodal structure and a pronounced shift following a consensus upgrade, reflecting heterogeneous deployments and diverse communication paths. These results demonstrate that mixture modeling of block time provides a practical and informative diagnostic tool for analyzing and monitoring high-performance BFT consensus.

Arthur Chagas, Pedro Bento, Yan Aquino, Arthur Buzelin · 6 authors

Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that matter for risk management. This paper proposes a model-agnostic audit framework for evaluating whether volatility forecasts remain reliable across latent market regimes. We learn time-series representations of market-state windows, cluster them into regimes using only training information, assign regimes out of sample, and compare aggregate forecast behavior with regime-conditional bias, tail-underprediction, and underprediction-sensitive economic losses. Applied to daily volatility forecasting across cryptocurrency and ETF assets, the audit shows that models with competitive aggregate accuracy can still exhibit substantial regime-specific bias and severe tail underprediction. The results suggest that volatility forecasting should be evaluated not only by average error, but also by where and how forecasts become unreliable. Our framework shifts forecast evaluation from asking which model is most accurate on average to identifying the market regimes in which apparently accurate forecasts fail conditionally. Reproducibility: https://github.com/arthurchagas1/Latent-Regime-Bias-Auditing-for-Volatility-Forecasting

Neha Kamboj, Vinita Choudhary, Sonal Trivedi

Blockchain technology, initially developed as a backbone for cryptocurrencies, has rapidly expanded into broader domains of finance and business. Its unique attributes – transparency, decentralization, immutability, and enhanced security – offer solutions to persistent challenges in financial services. This chapter examines blockchain applications beyond cryptocurrency, focusing on its role in transforming financial services such as Know Your Customer (KYC), cross-border payments, and compliance. A case-based exploration of blockchain-enabled KYC demonstrates how distributed ledgers can streamline identity verification while ensuring trust and efficiency. The chapter also outlines benefits, limitations, and potential applications, contributing to a holistic understanding for policymakers, financial institutions, researchers, and practitioners.

Mehmet Çınar, Muhammet Apak

This study investigates short-term predictive relationships between major cryptocurrencies—specifically Ethereum (ETH) and Solana (SOL)—and their respective sub-tokens (DYDX, UNI, GRT, JUP, RAY, PYTH) by employing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. Using high-frequency data across different time intervals (15 minutes, 1 hour, and 4 hours), the study examines whether price movements in major tokens are associated with enhanced short- and medium-term predictability of ecosystem-based sub-tokens. The empirical results indicate that the hybrid CNN-LSTM model achieves strong forecasting performance at shorter time horizons, while prediction accuracy declines as the time interval increases, reflecting the limiting role of market volatility. The results indicate statistically significant short-term predictive relationships and pronounced co-movement patterns between major token movements and sub-token pricing behavior, particularly at higher-frequency intervals, suggesting that Ethereum and Solana provide informative signals related to ecosystem-wide liquidity and trading activity.

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Ryan Anthony, Jechenthia Maria Taso, Stephen Yohanes Christopher, Ridhwan Ardiyansyah

This study aims to analyze and compare the performance of three algorithms, namely Support Vector Regression (SVR) with a linear kernel, XGBoost, and LightGBM, in predicting the Price of Ethereum cryptocurrency based on daily historical data. The study uses Ethereum Price data in USD for the last five years obtained from the investing.com website. The variables used are Close, Open, High, and Low Prices. The study uses two data splitting scenarios: 80% training data and 20% testing data, and 70% training data and 30% testing data. This study also uses time step variations to test the effect of time dependency on algorithm performance. The results indicate that the LightGBM algorithm has the best performance compared to the other two algorithms with an average MAE value for High Price of 75.486, SVR has a value of 115.590, and XGBoost has a value of 77.314 in the 80% training data and 20% testing data split. In the 70% training data and 30% testing data split, the LightGBM algorithm still excels with an average MAE value for High Price of 78.228, SVR of 104.356, and XGBoost of 83.573. Other evaluations such as RMSE and R2 also show the superiority of the LightGBM algorithm. For the required computation time, the SVR algorithm outperforms the other two algorithms.

Claudio Boido, Lewin Jones

Active asset managers increasingly include cryptocurrencies in their alternative asset allocations, highlighting their speculative and volatile nature. The aim of this research is to examine trends in the returns and volatility of cryptocurrencies, whilst accounting for the depegging of stablecoins, driven by speculative trading during macroeconomic shocks and technological shifts. We build a sample of market capitalisation, using data from the daily closing prices of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Ripple (XRP), two fiat-backed stablecoins (USDT and USDC), and a cryptocurrency-collateralised stablecoin (DAI). As a first step, a Granger-causality framework is applied to examine the influence of stablecoin depegging events on crypto returns during financial market stress. The results are strongly asymmetric: there is little evidence that depegs predict returns; whereas cryptocurrency returns robustly Granger-cause USDC depegging events, an effect that intensifies during periods of market stress. Stablecoin depegs appear to be a downstream symptom of cryptocurrency stress rather than a leading indicator of it. The analysis was extended by modelling volatility, using an EGARCH-X model to study whether depegs also affect crypto during periods of market stress and if larger deviations from the dollar peg are associated with higher cryptocurrency volatility, concentrated in the most liquid stablecoins (USDT and USDC), while the evidence for any change in this association during stress is limited. The findings carry implications for risk monitoring in digital-asset markets, where stablecoin behaviour reflects, rather than anticipates, cryptocurrency market conditions.

Avtandil Gagnidze, Maksim Iavich

Since 2008, when the cryptocurrency was first introduced under the name Satoshi Nakamoto, more and more people are interested in the «new money» – Bitcoin. Bitcoin is the first cryptocurrency and although many other cryptocurrencies were created and will be created in the future, Bitcoin remains the most popular cryptocurrency to this day. Naturally, along with the rapid growth of information technologies and their applications, many new «computerized» currencies will emerge. Because anyone can buy and sell cryptocurrency (e.g. bitcoin) and, thus, cryptocurrency is a subject of trade, hence cryptocurrency and in particular bitcoin is a product. Naturally, questions arise about the determinants of cryptocurrency price changes. In particular: Are the changes in the prices of cryptocurrency (and in particular Bitcoin) related to the development trends of the global economy? Are changes in the prices of cryptocurrency (and in particular Bitcoin) related to indicators of the state of the global economy, such as the well-known indices DJII, Nasdaq, S&P 500 and others. Thus it is interesting to see whether it is possible to predict changes in the prices of cryptocurrencies (and in particular Bitcoin) using different methods of time series.

RAZIULLAH SADID, Farzad AHMADİ

This study aims to provide a comprehensive science mapping and bibliometric analysis of the FinTech landscape within Islamic banking and finance. It deciphers the intellectual structure and thematic evolution of the field during the transformative window from 2017 to early 2026. Methodology: Utilizing the Scopus database, a dataset of 725 scholarly documents was extracted and analyzed. The research employs a multi-tool approach, integrating R-Bibliometrix (Biblioshiny) for longitudinal performance analysis and VOSviewer for visualizing keyword co-occurrence and institutional collaboration networks. The PRISMA 2020 protocol was followed to ensure methodological transparency. Findings. The results reveal an exponential surge in scientific production, characterized by an impressive annual growth rate of 28.42%. Malaysia and Indonesia emerge as the primary global knowledge hubs, with the International Islamic University Malaysia leading institutional contributions. The analysis identifies three core intellectual clusters: (1) Blockchain and Cryptocurrencies, (2) AI and Regulatory Compliance, and (3) Financial Inclusion and Institutional Stability. Thematic evolution indicates a strategic shift from basic FinTech adoption toward advanced applications in Artificial Intelligence, Ethical Technology, and the Sustainable Development Goals (SDGs). Originality,

Sascha Hägele

No abstract is available for this record.

Pouyan Esmaeilzadeh, Karlene Cousins, Hemang Subramanian, Arun Rai

No abstract is available for this record.

Nydia REMOLINA LEON, Aurelio GURREA-MARTINEZ, Daniel LIU

This article provides a comprehensive analysis of the treatment of digital assets in insolvency. Given that cryptoassets can be the subject of various transactions—including purchase, sale, custody, and lending—understanding their nature and implications in insolvency is relevant for any firm, not just cryptoexchanges. The article begins by offering a general overview of the world of cryptoassets. It then examines the nature of cryptoassets from accounting, financial, and legal perspectives. While much of the literature on insolvency and cryptoassets has primarily focused on the analysis of whether cryptocurrencies constitute property of the estate, this article explores additional issues, such as the treatment, role and rights of tokenholders in insolvency, the initiation of insolvency proceedings by cryptolenders, and the valuation, recovery, and realization of digital assets in bankruptcy. Such analysis is conducted from a comparative perspective, examining how jurisdictions around the world have addressed some of those issues and how cryptoassets have been used to engineer innovative solutions in restructuring agreements.

Avtandil Gagnidze, Maksim Iavich

With the improvement of data technology advances and the sharp addition of web customers number since the 90s, numerous computerized monetary standards are presented. the most popular among them is Bitcoin. It was decided to investigate the possible relations between the most popular cryptocurrency Bitcoin price dynamics and global Nasdaq index dynamics using Mathematical and Statistical methods. The main question is: Are the Bitcoin prices somehow related with Nasdaq Composite Index? We use both, Quantitative and Qualitative data analysis methods to answer this question: Namely, the Regression model and Non­-Parametric testing. According to Quantitative methods, it was found that there exists a correlation and the regression equation is not bed: it seems that it is possible to explain about 60% of changes in Bitcoin Prices by changes in the Nasdaq Index. According to Qualitative methods, it was found that these two variables are independent. In this case, the Qualitative conclusion is more likely to be right, and the correlation is most likely because of coincidence.

Vcc Business

Paying online often means sharing card details with merchants, advertising platforms, software providers, and payment processors. For freelancers, agencies, online sellers, and small teams, that can create unnecessary exposure: a compromised merchant account, an unexpected renewal, or a card number reused across several services may turn into a difficult cleanup project. A virtual card funded through a USDT top up offers another way to separate online spending from a primary bank account while keeping budgets easier to manage. This approach is not a promise of anonymity, approval, or freedom from verification. A responsible provider may still require identity checks, transaction monitoring, and information about the source of funds. The practical benefit is financial separation and control. Instead of giving every website direct access to a bank-linked card, you can use a dedicated card for approved online purchases, review the conversion terms, and keep records for accounting and compliance. Why use USDT to fund a virtual card USDT is a dollar-pegged digital asset commonly used to move value between supported wallets and platforms. When a card provider accepts USDT, it may convert the deposited amount into the card's spending balance, subject to its network, supported blockchain, confirmation requirements, fees, and compliance procedures. This can be useful for users who already hold USDT and want to pay merchants that accept ordinary card payments rather than cryptocurrency directly. The main operational advantage is separation. A dedicated virtual card can be assigned to advertising, SaaS subscriptions, supplier purchases, or a single project. If the card must be frozen or replaced, the issue may be contained to that spending channel instead of requiring changes across a personal bank account and every recurring payment connected to it. How the funding process usually works A typical flow has three stages: you create or select a card, send USDT to a deposit addre Full article attached as Markdown. Published for vccbusiness.com.

Fernando Frachone Neves, André Luiz Oliveira, Flávia Vancim Frachone MASSA, Tainara Adriani Ribeiro de Jesus · 5 authors

A proliferação da tecnologia blockchain e da mineração de criptomoedas tem gerado interesse de especialistas em sustentabilidade, emergindo um novo campo de estudo, desenvolvendo o conceito de criptomoedas verdes e a sustentabilidade digital. Neste sentido, este estudo realizou uma análise bibliométrica com o objetivo de mapear as tendências, estruturas temáticas e avanços na literatura científica sobre sustentabilidade digital no ecossistema blockchain, com foco em criptomoedas verdes. Para isso, foram analisados 133 artigos científicos extraídos do Web of Science (WOS), utilizando-se o software RStudio. Os resultados revelaram um crescimento acelerado de publicações, com um pico em 2024, indicando um campo de pesquisa em rápida expansão. As contribuições em pesquisa demonstram uma polarização, destacando a China e a Índia como principais polos. Temas dominantes incluem "cryptocurrency", "bitcoin", "blockchain technology", "green bonds", "clean energy" e "renewable energy", enquanto o mapeamento temático identificou "energy consumption", "risk" e "green challenges adoption" como temas motores. Esta revisão bibliométrica confirma o crescente interesse em criptomoedas verdes, impulsionado pela necessidade de mitigar impactos ambientais e alinhar a inovação tecnológica aos Objetivos de Desenvolvimento Sustentável (ODS) da ONU. Conclui-se que o estudo oferece percepções importantes aos formuladores de políticas, investidores e desenvolvedores, visando promover um desenvolvimento digital mais equitativo e alinhado à sustentabilidade.

Ayaan Siddiqui

Business Models and Value Creation via the x402 Protocol in Web3

Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat

Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.

Ningyu Zhou

This paper examines whether information stress affects trading frictions and liquidity resilience in cryptocurrency markets. Using public information proxies and OHLCV-based friction indicators, the analysis applies local projections to trace the response of trading conditions. The results show that information stress mainly affects trading frictions in the short run. Spread-based friction reacts immediately and recovers quickly, while the Amihud-based proxy adjusts more gradually. The cumulative effect is strongest in the early horizons and weaker over longer horizons. Overall, information stress creates temporary trading pressure rather than persistent deterioration in market resilience.

Jasroop Singh Narang

Cryptocurrencies have emerged as a significant component of the global financial system, offering new opportunities for digital transactions and financial innovation while raising concerns about their high price volatility and its implications for national economies. This study examines the impact of cryptocurrency volatility on national currency stability through a secondary analysis of existing macroeconomic literature. A systematic literature review was conducted using peer-reviewed journals, institutional reports, and credible academic sources to evaluate the relationship between cryptocurrency volatility and key macroeconomic indicators, including exchange rates, inflation, monetary policy,financial stability, and economic growth. The findings indicate that the macroeconomic effects of cryptocurrency volatility vary across countries depending on financial development, institutional quality, and regulatory frameworks. While cryptocurrencies promote financial innovation and cross-border transactions, they also pose challenges for monetary authorities and policymakers. The study concludes that the impact of cryptocurrency volatility is context-dependent and highlights the need for further empirical research, particularly in developing economies and emerging digital asset markets.

Marion Bonazzi

This country profile demonstrates how France plays a leading role in the FCT domain, ranking amongst the top beneficiaries of Horizon Europe funding, with approximately 11.4% of the total allocated budget. French stakeholders are highly engaged in cross-sector projects, frequently assuming coordination responsibilities or leading key work packages. A distinctive feature of France’s participation is the role of the Police Nationale and Gendarmerie Nationale, two key actors in many EU-funded security projects. Their involvement ensures a strong alignment between research activities and real-world law enforcement needs, particularly in areas such as Internal Security, Crisis Management, and the Protection of Public Spaces. Furthermore, a broad ecosystem of public research institutions, governmental bodies, and industrial partners contributes to the national effort. France’s research and innovation community is actively involved across a wide range of FCT priorities, including Artificial Intelligence, Cybersecurity, Border Management, and Digital Transformation. Beyond technological development, French actors also play a significant role in shaping regulatory frameworks and promoting the exchange of best practices, particularly in law enforcement and judicial cooperation. Overall, France shows strong participation in Horizon Europe, with representatives in over half of FCT-funded projects, particularly in activities related to the dark web and cryptocurrencies, the trafficking of humans and goods, and strong support for training and exercises. We’re collecting feedback on this report through the EU Survey Platform, if you’d like to share your thoughts please click on the link below. https://ec.europa.eu/eusurvey/runner/enact-report-feedback

Gloria Onyarin

The rapid digitalization of financial services has transformed the global financial ecosystem, enabling faster transactions, enhanced customer experiences, and greater financial inclusion. However, this digital transformation has simultaneously increased the complexity, scale, and sophistication of financial fraud. Traditional rule-based fraud detection systems often struggle to identify evolving fraud patterns, resulting in delayed responses, increased false positives, and substantial financial losses. Artificial Intelligence (AI)-powered real-time fraud monitoring systems have emerged as a transformative solution capable of detecting suspicious activities instantly through advanced data analytics, machine learning, deep learning, natural language processing, and behavioral intelligence. These systems continuously analyze vast volumes of transactional and non-transactional data, enabling financial institutions to identify anomalies, predict fraudulent behavior, and automate risk management processes with unprecedented accuracy and speed. This literature review examines the evolution, applications, technological foundations, benefits, challenges, and future directions of AI-powered real-time fraud monitoring systems in modern financial services. The review highlights how AI enhances fraud detection capabilities across banking, payment systems, insurance, digital wallets, cryptocurrencies, and investment platforms while discussing critical concerns related to privacy, algorithmic bias, explainability, cybersecurity, and regulatory compliance. The findings demonstrate that AI-driven fraud monitoring represents a fundamental component of modern financial security infrastructure and will continue to shape the future of fraud prevention in increasingly digital financial environments.

Leland Lazarus

This chapter analyzes the rise of Chinese “geocriminality” in Latin America and the Caribbean, arguing that Chinese transnational organized crime cannot be understood separately from deepening China–LAC economic and migratory ties. Moving beyond a narrow law-enforcement lens, it introduces the concept of a “Silk Road of Crime,” in which Chinese criminal networks exploit the same trade routes, diaspora communities, financial systems, and regulatory gaps that underpin legitimate engagement. Drawing on investigations by Earth League International, ProPublica, and others, the chapter documents four converging illicit markets: fentanyl precursor trafficking to Mexican cartels, low-cost “Flying Money” laundering schemes, wildlife trafficking (including jaguar parts and shark fins), and human smuggling along “the route” from China through Latin America to the US border. It details networks linked to Fujian-based groups and triads, highlighting patterns of crime convergence and the use of cryptocurrencies, shell companies, and diaspora businesses. While direct CCP command-and-control links remain limited, the chapter argues that selective enforcement, corruption, and strategic ambiguity create permissive conditions. Ultimately, Chinese geocriminality reflects the dark side of complex interdependence, reinforcing dependency structures and complicating US, Chinese, and regional cooperation.

Maja CZYŻEWSKA

This paper empirically compares four architectures for financial time-series forecasting: LSTM, CNN, the original Regularized Self Attention Regression (RSAR) model, and a multiobjective optimized RSAR variant, denoted MO-RSAR, obtained using the Non dominated Sorting Genetic Algorithm II (NSGA-II). The models are evaluated on six datasets covering Forex, equity index and cryptocurrency markets, for short and long horizons. All models share a common preprocessing pipeline and evaluation framework and are assessed using standard error metrics, with emphasis on Mean Absolute Percentage Error (MAPE). MORSAR yields the lowest average prediction error across all datasets and provides significant gains for longer, more volatile horizons, while simpler architectures remain competitive for short-term forecasts. The key methodological contribution is the first empirical integration of the RSAR architecture with NSGA-IIbased multi-objective hyperparameter optimization for financial time-series forecasting. The proposed framework treats RSAR configuration as a bi-objective search over accuracy and generalization (via the train-validation gap), and evaluates the resulting model under a unified protocol across heterogeneous markets and horizons.