Machine Learning (ML) in distributed environments increasingly deals with sensitive data (like healthcare or financial records) that cannot be centrally stored or processed due to privacy concerns. Federated Learning (FL) addresses this by enabling model training across decentralized devices, but faces significant challenges including system reliability, node failures, and trust issues among participants. Traditional FL approaches often rely on centralized coordinators, creating single points of failure and potential security vulnerabilities. This paper presents a novel approach to FL that leverages smart contracts, blockchain, and decentralized storage to enhance the traceability and reliability of the learning process. Our proposed system architecture is fully decentralized, eliminating single points of failure and promoting cooperation through a rewarding mechanism. Unlike previous approaches that neglect node fault tolerance, we introduce a smart contract based scheme for managing node failures and electing the aggregator node. The presence of the smart contract, executed on a decentralized permissioned blockchain, provides reliability guarantees and eliminates the need for costly distributed algorithms in terms of message exchange. An experimental study is conducted to evaluate various aspects of the FL system. We present results related to the accuracy and effectiveness of the FL system on ML models. We also examine the performance related to the distribution of the weights of the ML model based on the use of IPFS. Furthermore, we analyze the performance of the smart contract in terms of gas consumption. Lastly, we investigate the impact of failures combined with incentive policies and aggregator election algorithms on the FL system. Our findings demonstrate the viability of the proposed approach, paving the way for more robust, reliable, and efficient FL systems.
Essossinam Pali, Coffi Cyprien Aholou, François Paul Yatta
Since 2019, Togo has resolutely engaged in the decentralization process marked by communalization and elections of municipal councilors. Financial autonomy constitutes an essential lever for the free administration of municipalities, allowing them to ensure decision-making and the implementation of development projects. However, despite a legal and regulatory framework defining taxation specific to local authorities, Togolese municipalities are often perceived as needing more financial resources. This study aims to map the financing mechanisms for decentralization in Togo and analyze their contribution to municipal budgets. By adopting a quantitative approach combining documentary analysis and interviews with 188 experts and practitioners of local finance from various Togolese structures, four main financing mechanisms were identified: local, national, Community, and international. Among these mechanisms, own resources (in particular from the sale of products and services, fiscal and non-fiscal taxes) and state transfers via the Support Fund for Local Authorities emerge as the primary sources of financing for municipalities. However, the study reveals that several instruments of local mechanisms, although institutionally defined, still need to be updated in many municipalities, thus limiting their effectiveness in resource mobilization. These results highlight the importance of optimizing the management of local mechanisms to strengthen municipalities’ financial autonomy and support territories’ sustainable development.
Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype.
The integration of blockchain technology with the Internet of Things (IoT) introduces significant scalability, energy efficiency, and security challenges, particularly when using traditional consensus mechanisms like Proof of Work (PoW). IoT networks generate vast amounts of data while operating under resource constraints, necessitating the development of consensus algorithms that balance energy efficiency, transaction throughput, and security. Addressing these challenges is critical for the sustainable adoption of blockchain in IoT ecosystems. This research aims to enhance blockchain scalability and performance in IoT environments through the development of the Enhanced Efficient Proof of Stake (EePoS) consensus algorithm. The objective is to provide a framework that optimizes validator selection, minimizes energy consumption, and ensures robust security against common blockchain threats. The proposed method employs a multi-layered architecture, selective validation, and a behavior-aware penalty-reward system to ensure efficient consensus. Key security metrics, including Probability of Successful Attack (PSA) and Forking Rate (FR), were evaluated to demonstrate the algorithm’s resilience. EePoS reduces PSA by dynamically adjusting validator selection based on stake, behavior, and transaction load while decreasing FR through cluster-based voting and hierarchical aggregation. Experimental results demonstrated 20% lower PSA, 30% reduced FR, and 8% faster consensus time compared to ePoS. Throughput improved to 296 TPS while reducing CPU and memory utilization, ensuring robust performance for resource-constrained IoT networks. The novelty of this work lies in the tailored enhancements to the PoS framework, specifically designed for IoT constraints, making EePoS a scalable, energy-efficient, and secure solution for IoT blockchain integration.
Khoirul Hidayah, Muhammad In’am Esha, Dwi Hidayatul Firdaus, Ramadhita Ramadhita
The Non-Fungible Token (NFT) is one form of trade utilising crypto assets as a medium of exchange. This system has proven effective in assisting creators in protecting both their economic and moral rights. However, the existence of Regulation of the Minister of Finance No. 68/PMK.03/2022 concerning Value Added Tax and Income Tax on Cryptocurrency Trading does not adequately address the phenomenon of NFT trading. This raises an intriguing issue regarding the formulation of tax collection for NFTs as digital assets that can be traded and serve as a source of state revenue. This study employs a socio-legal approach with qualitative methods. Based on an analysis of legislation, the theory of justice, and tax collection theory, three alternative models for regulating income tax and VAT on NFTs in Indonesia are proposed. The first model suggests specific regulation in the form of a Minister of Finance Regulation. The second model recommends classifying NFT trading platforms as Permanent Establishments (PE). The third model advocates for the application of tax treaties to prevent double taxation. This study is expected to contribute to the development of NFT taxation regulations in Indonesia.
Abstract Since its introduction as a decentralized digital currency for peer-to-peer transactions, Bitcoin’s role in financial markets has undergone significant evolution. We employ bibliometric analysis to explore research trends in Bitcoin, identifying two primary perspectives in the recent financial economic literature: Bitcoin as a speculative asset and as a safe-haven asset. The speculative nature of Bitcoin is evident through its high volatility and frequent price jumps, largely influenced by rapid shifts in investor sentiment and attention, which create both risks and opportunities for traders. Conversely, Bitcoin exhibits characteristics of a safe-haven asset due to its asymmetric tail dependence and negative correlation within certain asset classes.
Yuri Bespalov, Lyudmila Kovalchuk, Hanna Nelasa, Roman Oliynykov
Abstract Decentralized consensus protocols have a variety of parameters to be set during their deployment for practical applications in blockchains. The analysis given in most research papers proves the security state of the blockchain, at the same time usually providing a range of acceptable values, thus allowing further tuning of the protocol parameters. In this paper, we investigate Ouroboros Praos, the proof-of-stake consensus protocol deployed in Cardano and other blockchains. In contrast to its predecessor, Praos allows multiple honest slot leaders that lead to fork creation and resolution, consequently decreasing the block rate per time unit. In our analysis of dependence on protocol parameters such as active slot coefficient and p2p network block propagation time, we obtain new theoretical results and explicit formulas for the expectation of the length of the longest chain created during the Praos epoch, the length of the longest unintentional fork created by honest slot leaders, the efficiency of block generation procedure (the ratio of blocks included in the final longest chain vs the total number of created blocks), and other characteristics of the blockchain throughput. We study these parameters as stochastic characteristics of the block generation process. The model is described in terms of the two-parametric family ξ ij of independent Bernoulli random variables which generate deformation of the binomial distribution by a positive integer parameter—the delay (deterministic or random). An essential part of our paper is a study of this deformation in terms of denumerable Markov chains and generating functions.
Mohammad A Razzaque, Shafiuzzaman K Khadem, Sandipan Patra, Glory Okwata · 5 authors
This paper presents a systematic review of recent advancements in V2G cybersecurity, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for detailed searches across three journal databases and included only peer-reviewed studies published between 2020 and 2024 (June). We identified and reviewed 133 V2G cybersecurity studies and found five important insights on existing V2G cybersecurity research. First, most studies (103 of 133) focused on protecting V2G systems against cyber threats, while only seven studies addressed the recovery aspect of the CRML (Cybersecurity Risk Management Lifecycle) function. Second, existing studies have adequately addressed the security of EVs and EVCS (EV charging stations) in V2G systems (112 and 81 of 133 studies, respectively). However, none have focused on the linkage between the behaviour of EV users and the cybersecurity of V2G systems. Third, physical access, control-related vulnerabilities, and user behaviour-related attacks in V2G systems are not addressed significantly. Furthermore, existing studies overlook vulnerabilities and attacks specific to AI and blockchain technologies. Fourth, blockchain, artificial intelligence (AI), encryption, control theory, and optimisation are the main technologies used, and finally, the inclusion of quantum safety within encryption and AI models and AI assurance (AIA) is in a very early stage; only two and one of 133 studies explicitly addressed quantum safety and AIA through explainability. By providing a holistic perspective, this study identifies critical research gaps and outlines future directions for developing robust end-to-end cybersecurity solutions to safeguard V2G systems and support global sustainability goals.
To address the risks of validator centralization, Proposer-Builder Separation (PBS) was introduced in Ethereum to divide the roles of block building and block proposing, fostering a more equitable and decentralized block production environment. PBS creates a two-sided market in which searchers submit valuable bundles to builders for inclusion in blocks, while builders compete in auctions for block proposals. In this paper, we formulate and analyze a role-selection game that models how profit-seeking participants in PBS strategically choose between acting as searchers or builders, using a co-evolutionary framework to capture the complex interactions and payoff dynamics in this market. Through agent-based simulations, we demonstrate that agents' optimal role-acting as searcher or builder-responds dynamically to the probability of conflict between bundles. Our empirical game-theoretic analysis quantifies the equilibrium frequencies of role selection under different market conditions, revealing that low conflict probabilities lead to equilibria dominated by searchers, while higher probabilities shift equilibrium toward builders. Additionally, bundle conflicts have non-monotonic effects on agent payoffs and strategy evolution. Our results advance the understanding of decentralized block building and provide guidance for designing fairer and more robust block production mechanisms in blockchain systems.
We introduce Efficient LLM Token Extraction (ELTEX), a framework addressing the critical challenge of LLM domain specialization by systematically extracting and integrating domain indicators throughout synthetic data generation. Unlike approaches relying on implicit knowledge transfer, ELTEX explicitly leverages domain signals to maintain specialized knowledge integrity. In our cybersecurity case study, ELTEX-enhanced data enables a fine-tuned Gemma-2B model to achieve performance competitive with GPT-4o on blockchain cyberattack classification while reducing computational requirements. Our Google Sheets implementation makes ELTEX accessible to non-technical users. Our contributions include: (1) the ELTEX framework; (2) Google Sheets Add-on implementation; (3) empirical validation showing how ELTEX bridges performance gaps between small and large models; and (4) a synthetic dataset of 11,448 texts for blockchain cyberattack detection.
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convolutions, and attention mechanisms address domain-specific challenges and enhance feature representation and computational efficiency. Structural refinements such as spatial-channel exploitation, multi-path design, and feature-map enhancement contribute to robust hierarchical feature extraction and improved generalization, particularly through transfer learning. Efficient preprocessing strategies, including Fourier transforms, structured transforms, low-precision computation, and weight compression, optimize inference speed and facilitate deployment in resource-constrained environments. This survey presents a unified taxonomy that classifies CNN architectures based on spatial exploitation, multi-path structures, depth, width, dimensionality expansion, channel boosting, and attention mechanisms. It systematically reviews CNN applications in face recognition, pose estimation, action recognition, text classification, statistical language modeling, disease diagnosis, radiological analysis, cryptocurrency sentiment prediction, 1D data processing, video analysis, and speech recognition. In addition to consolidating architectural advancements, the review highlights emerging learning paradigms such as few-shot, zero-shot, weakly supervised, federated learning frameworks and future research directions include hybrid CNN-transformer models, vision-language integration, generative learning, etc. This review provides a comprehensive perspective on CNN's evolution from 2015 to 2025, outlining key innovations, challenges, and opportunities.
Introduction Technological change is a mega trend that drives sustainable development in the agrifood sector globally. The introduction of BanQu, a blockchain-enabled platform, aimed to address challenges like lack of transparency, side-selling, and unfair pricing in Uganda's barley value chain, but its acceptance has been slow. While blockchain adoption has thrived in developed countries and large supply chains, empirical evidence on its uptake among smallholder farmers in Sub-Saharan Africa, especially Uganda, remains limited. This study investigates determinants of smallholder barley farmers' intentions to accept blockchain technology (BCT) in Uganda. Methods The study utilized the second extension of the Technology Acceptance Model (TAM2), customized to fit Uganda's context. Quantitative data were gathered from 245 farmers in Bukwo and Kween, the two leading barley-producing districts in eastern Uganda. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results and discussion The study showed that perceived usefulness (PU) significantly influenced behavioral intention (BI) and shaped perceived ease of use (PEU). Subjective norms (SUN) and voluntariness (BV) enhanced PU, while perceived behavioral control (PBC) improved PEU. Notably, BCT relevance (BR) directly influenced BI, bypassing PU. These findings provide fresh insights into rural technology adoption, highlighting PU's influence on PEU and BV's role in shaping PU. The study recommends emphasizing BCT benefits such as reducing transaction costs, leveraging social networks, and addressing resource gaps to boost acceptance. This study advances understanding of BCT adoption among smallholder farmers in emerging economies like Uganda.
Syeda Fizza Abbas, Sumiya Tahir, Sayyid Haider Mustafa Rizavi
This study examines the financial performance of diversified portfolios composed of various asset categories, including green cryptocurrencies, non-green cryptocurrencies, energy cryptocurrencies, stocks of leading companies, stocks of top energy companies, and stocks of prominent sustainable companies within the context of G7 nations. Additionally, it investigates the financial performance of green and non-green cryptocurrency portfolios across these regions. It aims to compare returns while examining the initiatives undertaken by these countries to foster sustainable financial systems. The research also explores how investors can leverage portfolio optimization to enhance returns in the rapidly evolving digital currency market. The study employs two machine learning techniques. First, six constraints, including maximum Sharpe ratio, minimum variance, maximum return, Sortino ratio, and Black-Litterman model, were applied to build portfolios for green and non-green cryptocurrencies. The model started with an 80%-20% train-test separation to find suitable allocations that it improved using full dataset retraining. The results explained that the highest Sharpe ratio portfolio generated the finest performance in the U.S. and Japan because of their strong financial market institutions and active participation from institutions. The investment cultures of Canada and Italy led to their selection of minimum variance portfolios. The Black-Litterman model worked well in the UK to produce equilibrium between market expectations and real risk-returns while German investors chose maximum return portfolios due to their risk tolerance. The French financial industry put risk-adjusted returns at the forefront thus the optimized Sortino ratio strategy proved most appropriate. A comparison between green and non-green portfolios shows that green portfolios regularly exhibited lower volatility together with superior risk-adjusted returns especially when sustainability policies were clearly defined in the nation. The higher returns from non-green portfolios came alongside higher speculative risk which made them susceptible to market volatility. This study demonstrated that selecting portfolios should be done based on specific market features that vary from country to country. Those who need stable long-term returns can achieve it through green investing while investors with high tolerance for risks can spend in non-green investments. Future studies should concentrate on developing dynamic rebalancing methods for portfolios while integrating decentralized finance (DeFi) technology to optimize portfolio management systems.
Nurul Ain Ahmad, Nurul Syafiqah Imran, Nur Ruwaidah Kairuddin, Norhazlin Zafira Kirman · 6 authors
Forex Trading merupakan satu aktiviti perniagaan dari segi tukar beli matawang. Terdapat ramai individu yang menggunakan kaedah Forex Trading ini untuk berdagang mata wang dan bertujuan untuk mendapatkan keuntungan. Bitcoin pula merujuk kepada alat pengukuran nilai yang digunakan dalam kaedah Forex Trading. Secara umumnya, artikel ini akan memfokuskan perbincangan tentang maksud Forex Trading, konsep mata wang kripto iaitu Bitcoin dan konsep riba. Selain itu, artikel ini telah mengkaji metodologi yang digunakan oleh penulis iaitu secara penyelidikan kualitatif. Penulis akan memastikan semua sumber-sumber yang digunakan adalah relevan dan berkaitan dengan penyelidikan supaya mudah dan sesuai untuk dijadikan rujukan. Di samping itu, terdapat perselisihan pendapat tentang hukum perdagangan Bitcoin berdasarkan kaedah Forex Trading mengikut syariah Islam. Artikel ini bertujuan untuk menjelaskan hukum-hukum penggunaan Bitcoin dan menerangkan beberapa pandangan ulama terhadap isu yang boleh dibangkitkan terhadap mata wang Bitcoin. Hasil perbincangan daripada artikel ini, jelaslah tentang hujah-hujah berkaitan hukum penggunaan Bitcoin sebagai alat pengukuran nilai dan terdapat beberapa langkah bagi mengatasi masalah yang berkaitan dengan penggunaan Bitcoin melalui kaedah Forex Trading.
This comprehensive article explores the rapid advancement of financial technologies (FinTech), highlighting their transformative role in enhancing transaction efficiency and security across global financial markets. The integration of artificial intelligence and machine learning in financial services has revolutionized fraud detection, credit assessment, and customer service delivery while presenting new implementation challenges. As digital payment systems and banking platforms continue to evolve from early electronic transfers to sophisticated mobile applications and neobanks, they reshape traditional financial models and expand access to previously underserved populations. The interplay between emerging technologies like distributed ledger systems, cloud computing, and biometric authentication creates a dynamic ecosystem where established institutions and innovative startups both compete and collaborate. Regulatory frameworks worldwide adapt to balance innovation facilitation against consumer protection, while specialized compliance technologies address increasingly complex requirements. Despite cybersecurity threats including data breaches and ransomware attacks, advanced security measures provide essential protection for the digital financial landscape.
HIV/AIDS continues to pose a significant public health challenge in Africa, with Sub-Saharan Africa accounting for the majority of global cases. While international donor funding has historically underpinned HIV/AIDS programs across the continent, the declining availability of external resources has emphasized the need for sustainable domestic financing. Local governments, situated at the intersection of national policies and community-level implementation, play a pivotal role in bridging this funding gap. This review examines the contributions of local governments to HIV/AIDS program funding in Africa through a comparative lens, focusing on successes, challenges, and opportunities for strengthening their role. Case studies from South Africa, Uganda, Nigeria, and Kenya highlight diverse approaches to resource mobilization, policy implementation, and community engagement. Persistent barriers, including limited fiscal capacity, donor dependency, weak governance structures, political instability, and competing priorities, are analyzed to inform strategic recommendations. The findings underscore the need for enhanced fiscal decentralization, capacity building, and innovative financing mechanisms to empower local governments in sustaining HIV/AIDS responses. By fostering greater local government participation, Africa can achieve more resilient and effective health systems, ensuring progress toward ending AIDS as a public health threat by 2030. Keywords: HIV/AIDS funding, Local government, Sustainable financing, Sub-Saharan Africa, Public health policy.
Popular technologies such as blockchain and zero-knowledge proof, which have already entered the enterprise space, heavily use cryptography as the core of their protocol stack. One of the most used systems in this regard is Elliptic Curve Cryptography, precisely the point multiplication operation, which provides the security assumption for all applications that use this system. As this operation is computationally intensive, one solution is to offload it to specialized accelerators to provide better throughput and increased efficiency. In this paper, we explore the use of Field Programmable Gate Arrays (FPGAs) and the High-Level Synthesis framework of AMD Vitis in designing an elliptic curve point arithmetic unit (point adder) for the secp256k1 curve. We show how task-level parallel programming and data streaming are used in designing a RISC processor-like architecture to provide pipeline parallelism and increase the throughput of the point adder unit. We also show how to efficiently use the proposed processor architecture by designing a point multiplication scheduler capable of scheduling multiple batches of elliptic curve points to utilize the point adder unit efficiently. Finally, we evaluate our design on an AMD-Xilinx Alveo-family FPGA and show that our point arithmetic processor has better throughput and frequency than related work.
The cryptocurrency market, known for its inherent volatility, has been significantly influenced by external shocks, particularly during periods of global crises such as the COVID-19 pandemic and the Russia–Ukraine war. This study investigates the volatility of the top seven cryptocurrencies by market capitalization—Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), USD Coin (USDC), XRP, and Cardano (ADA)—from 1 January 2020 to 1 September 2024, employing a range of GARCH models (GARCH, EGARCH, TGARCH, and DCC-GARCH). This research aims to examine the persistence of leverage effects, volatility asymmetry, and the impact of past price fluctuations on future volatility, with a particular focus on how these dynamics were shaped by the pandemic and geopolitical tensions. The findings reveal that past price fluctuations had a limited impact on future volatility for most cryptocurrencies, although leverage effects became evident during market anomalies. Stablecoins (USDC and USDT) showed a distinct volatility pattern, reflecting their peg to the US Dollar, while platform-associated BNB demonstrated unique volatility characteristics. The results underscore the market’s sensitivity to price movements, highlighting the varying reactions of investor profiles across different cryptocurrencies. These insights contribute to understanding volatility transmission within the cryptocurrency market during times of crisis and offer important implications for market participants, particularly in the context of risk management strategies.
The banking industry is experiencing a swift transformation fueled by technological advancements, including artificial intelligence (AI), blockchain, and automation, which are redefining financial services. The rise of the metaverse offers banks new avenues to boost customer engagement, provide immersive financial experiences, and create innovative digital products. This paper delves into the effects of technological innovation on banking, focusing on how the metaverse can be integrated into banking business models. It looks at the advantages of virtual banking branches, decentralized finance (DeFi), and tailored financial services, while also tackling significant challenges like cybersecurity threats, regulatory issues, and obstacles to consumer adoption. By analyzing existing literature and industry trends, this study underscores the metaverse's potential to transform banking, while stressing the importance of strong security measures and regulatory frameworks. The findings indicate that banks need to embrace a hybrid strategy that balances innovation with compliance and risk management to effectively navigate the changing digital landscape.
Alex Veith, Patrick R. Carney, Aiqing Wu, Brenda L. Rojas · 9 authors
Scientific progress benefits from the sharing of "research assets" such as data, reagents, models, and experimental samples. To improve asset shareability, we evaluated the availability, quality, and characterization of recombinant DNA molecules, recombinant mouse models, and tissue samples described by our laboratory in ten publications spanning over thirty years. Employing state-of-the-art molecular technologies, we identified existing samples, updated their localization, generated modern sequences and maps of recombinant models, and ported the associated metadata to an internal blockchain-dependent resource using a standardized description for each asset class. We also created non-fungible tokens representing research assets on the public blockchain network Solana. In addition to providing an audit of previously reported shareable assets and improving the value of recombinant models, this re-analysis also provides evidence for the utility of extant tissue samples that may be difficult and expensive to regenerate. The results demonstrate how retrospective analysis can improve and expand upon the spectrum of shareable research assets through updates on molecular characterization and physical location, as well as improving the availability of biological samples of potential high experimental value. Moreover, the development of a decentralized ledger harboring this revised metadata provides a path to the description and tokenization of scientific assets and provides a strategy to extend the life of scientific assets even after laboratories or sources close.
Abstract During the last years, financial market contagion has become a critical concern for policymakers and investors, particularly with respect to the financial stability of cryptocurrency platforms. This paper explores the contagion effect among crypto exchanges employing the Susceptible–Infected–Recovered (SIR) model with time delay and investigates possible cooperative strategies. The SIR dynamical system is integrated with the replicator equation of evolutionary game theory to study the interplay between the spread of risk and the propensity of cryptocurrency platforms to become cooperative under the pressure of financial contagion. Different equilibrium points which correspond to both pure and mixed cooperative strategies characterize the resulting model. We carry out a theoretical analysis of the problem by studying the asymptotic behavior in the steady state. In addition, using extensive cryptocurrency market data from 2017 to 2023, we identify the key factors driving contagion and assess the dynamics of cooperative versus non-cooperative behavior. Our findings point out that cooperative strategies are essential to ensure financial stability, particularly in the long term, as they mitigate systemic risks and foster resilience. These results provide critical insights for policy makers and investors, offering actionable strategies to enhance the robustness of crypto markets and address the growing challenges of financial contagion in the digital asset ecosystem.
Abstract This paper examines the dependence, systemic risk spillover, return and volatility spillover, and portfolio implications across various timescales between the Green Bond (GB) and U.S. S&P 500 Stock (SP), Vanguard Total World Stock Index Fund (VT), Bitcoin (BTC), Ethereum (ETH), Ripple, OIL, and GOLD markets. The sample period is August 07, 2015–October 6, 2023, covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict. Using the wavelet–copula–conditional value-at-risk and wavelet-multivariate asymmetric-GARCH framework, our main results show that the systemic risk and return, volatility spillovers, and diversification opportunities are portfolio-specific and timescale-dependent. Specifically, there is a negative long-term correlation for the pairs GB-SP and GB-OIL, whereas the pair GB–GOLD pair is positively correlated in the short term. GB can mitigate the risk of other markets. In terms of the portfolio implications, GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term. Regarding hedging effectiveness, the role of GB for GOLD and VT is noted.
This study explores the transformative potential of blockchain technology in revolutionizing cross-border payment systems. Traditional methods are hindered by inefficiencies such as high transaction fees, prolonged processing times, and opaque operations, which impede seamless global financial interactions. Blockchain, with its decentralized and immutable ledger, offers a secure and transparent alternative that can significantly streamline payment processes. This paper examines how blockchain can facilitate real-time settlements, eliminate intermediaries, and enhance data integrity, thereby reducing costs and improving efficiency. Further, it addresses the practical applications and regulatory challenges associated with integrating blockchain into existing payment infrastructures. Ultimately, this research aims to provide actionable insights for developing a more efficient, transparent, and cost-effective cross-border payment ecosystem.