Legacy, profit-driven organizational structures often lack the adaptability, equity, and innovation required for the evolving complexities of contemporary patient-centered care. They can limit access, constrain options, impose financial burdens, and hinder optimized delivery. Here, we propose a new architecture for metaversal integrative healthcare using decentralized autonomous organizations (DAOs). Blockchain technology and smart contracts underpin equitable, transparent, and resilient health ecosystems beyond institutional gatekeeping and entrenched hierarchies. Central to this innovation is the introduction of Ecosystem Value Networks (EVNs), which quantify how diverse contributors, including patients, function as “wellness stewards” within decentralized, interconnected networks of value. EVNs represent a paradigm shift from transactional, top-down authority structures toward relational, emergent, and self-perpetuating interconnected systems. By programming prosocial principles into the architecture of DAOs via governance tokens, digitally encoded collective voting, autonomous smart contract enforcement, and transparency-by-design; these dynamics intrinsically reinforce collaborative over competitive behaviors and outcomes. Traditional institutions often reward dominance, prestige, and hierarchy, that represent conditioned patterns rooted in ancestral scarcity-based modes of fear and control; ill-suited for equitable healthcare. DAOs and EVNs, in contrast, condition new modes of operation grounded in abundance, interdependence, and shared stewardship. Over time, these digital frameworks can transform how health knowledge is generated, distributed, and enacted, establishing a “new normal” from the inside out, in which interconnectivity defines leadership and value. Moving from vision to strategy, this work maps a pathway toward truly participatory (opposed to nominal or symbolic), adaptive, and value-responsive healthcare systems for the new digital age.
Open access
Blockchain Technology Applications and Security
Legal, Health, Environmental and COVID-19 Challenges
<p>Existing financial systems are bloated with inefficiencies in their operation, lack of transparency and are characterized by and fallible and fragile accumulation points, whereas emerging decentralized finance (DeFi) platforms lack intelligent risk management, self-adaptive governance and provable security assurances. This paper proposes the Intelligent, Verifiable Financial Ledger (IVFL), a novel framework that harmoniously converts both Artificial Intelligence (AI) and blockchain to counteract their core drawbacks. AI-based smart contracts of a formally verifiable character that allows the intelligent, secure and auditable automated execution of complex financial transactions an agile and informed governance system, which is represented by the use of AI enhancements to the Decentralized Autonomous Organization (DAO). Simulation analysis shows that the IVFL framework enables substantial enhancements compared to baseline models, such as detecting anomalies with over 95% accuracy, decreasing operational overhead by 40 percent and becoming less vulnerable to coordinated network attacks. Coming back to provable security and adaptive intelligence, the IVFL framework represents a credible way of creating financial systems.</p>
Ke Zhang, Xiaoning Zhao, Chaocheng Zheng, Jiahong Ning · 8 authors
This study proposes Tool-RoCo, a novel benchmark for evaluating large language models (LLMs) in long-term multi-agent cooperation based on RoCo, a multi-robot cooperative benchmark. Recent research on LLM-based multi-agent systems has relied on predefined orchestration, while ignoring agent autonomy. Tool-RoCo treats other agents as tools and introduces cooperative tools, leveraging tool usage to evaluate multi-agent cooperation and self-organization. Tool usage means that each agent (LLM) selects a tool from a candidate set based on the current state, receives feedback, and adjusts its selection in subsequent rounds. To evaluate different autonomy levels, we propose four LLM paradigms: (1) centralized cooperation, where a single LLM allocates tools to all agents; (2) centralized self-organization, where a central LLM autonomously activates agents while keeping others inactive; (3) decentralized cooperation, where each agent has its own LLM and calls tools based on local information; and (4) self-organization, where a randomly chosen initial agent can request collaboration, activating additional agents via tool calls. Tool-RoCo includes three multi-robot tasks, SORT, PACK, and CABINET, to measure format and parameter accuracy and agent coordination through tool usage. The results using several LLMs showed that cooperative tools accounted for only 7.09% of all tools, indicating that LLM-based agents rarely invoked others as assistants. Moreover, activation tools accounted for 96.42%, suggesting that current LLMs tend to maintain active agents while seldom deactivating them for adaptive coordination. Tool-RoCo provides a systematic benchmark to evaluate LLM autonomy and cooperation in multi-agent tasks. Code and Demo: https://github.com/ColaZhang22/Tool-Roco
Distributed Ledger Technology (DLT) as a principle of corporate governance represents an institutional shift of the law of the firm. Once relegated to academic theorizing and cryptocurrency, DLT now forms institutional infrastructure with a nascent market of tokenized real-world assets (RWAs) surpassing $33B at the close of Q4 2025. This paper analyzes how DLT intersectors three pillars of management - Strategic, Operational and Financial - in conjunction with Transaction Cost Economics (TCE) and Agency Theory that also coincide with inextricably lower baseline costs of trust and coordination. Strategically, Decentralized Autonomous Organizations (DAOs) and Intellectual Property Non-Fungible Tokens (IP-NFTs) are increasingly at the forefront of governance and R&D-related compensation structure. Operationally, smart contracts govern supply chains at near-real time with the Global Shipping Business Network (GSBN) going live with container tracking implementations and the FDA implementing pilot programs for near-instant visibility into temperature-controlled shipping needs. Financially, treasuries and debt instruments are increasingly tokenized to allow firms to harness an illiquidity premium while equitizing their working capital. Ultimately, this research concludes that the international financial architecture is bifurcated as high-stable assets transition to permissioned DLTS while high-velocity assets remain in public programmable spaces.
There has been an exponential rise of Internet of Things (IoT) devices and autonomous systems, which have thrown light on the weaknesses of centralized cloud computing, especially in latency, bandwidth, and security. This paper will solve such problems by suggesting an integrated blockchain-edge architecture, which uses distributed trusting mechanisms to protect and optimize edge networks. The process of the methodology consists of four steps: architectural modeling, lightweight consensus design, performance-security trade-off analysis, and real-life validation. Experiments with iFogSim and BlockSim showed that edge networks enhanced with blockchain cuts latency and bandwidth consumption by 37 and 36 percent respectively compared to cloud-centric models. Consensus protocols such as Practical Byzantine Fault Tolerance (pBFT), Proof-of-Elaboration (PoE) and Leased Proof-of-Stake (LPoS) were designed and tested, using much less energy and having much faster transaction finality compared to Proof-of-Work. High resilience to Sybil, tampering, and 51% attacks was proven with Raspberry Pi clusters, and an 8% latency trade-off was observed, when smart contracts were used to enforce automated access control. Lastly, experimental validation with healthcare and industrial IoT datasets demonstrated that blockchain decreased attempts to access information unauthorized to nearly zero in the healthcare industry and minimized manipulations with machine logs by 70 percent in the industrial IoT. These results highlight blockchain-edge convergence as a potential direction towards the construction of scalable, secure and trustful decentralized systems.
The convergence of artificial intelligence (AI) and decentralized web technologies represents a pivotal shift in digital infrastructure, giving rise to the concept of AI-native protocols. These protocols integrate AI capabilities directly into their fundamental design, moving beyond mere application-level AI to create intelligent, adaptive, and autonomous decentralized systems. This paper explores the transformative potential of AI-native protocols in reshaping the decentralized web, often referred to as Web3. We delve into the architectural implications, key benefits such as enhanced security, efficiency, and scalability, and the profound societal impact of such a paradigm shift. Through a comprehensive literature review, we identify existing challenges in both AI and blockchain domains that AI-native protocols are uniquely positioned to address, including algorithmic bias, data privacy, and consensus mechanism inefficiencies. We propose a conceptual framework for designing these protocols, emphasizing core components like intelligent consensus, autonomous agents, and AI-powered smart contracts. Furthermore, the paper discusses the ethical considerations inherent in embedding AI within decentralized governance structures and outlines future research directions for fostering responsible innovation. Our findings suggest that AI-native protocols are not merely an incremental improvement but a foundational evolution that promises to unlock unprecedented levels of intelligence and autonomy across the decentralized digital landscape, fostering a more robust, equitable, and resilient internet.
Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.
Matteo Bjornsson, Taylor Hardin, Taylor Heinecke, Marcin Furtak · 6 authors
Distributed ledger technologies (DLTs) rely on distributed consensus mechanisms to reach agreement over the order of transactions and to provide immutability and availability of transaction data. Distributed consensus suffers from performance limitations of network communication between participating nodes. BLOCKY ZipperChain guarantees immutability, agreement, and availability of transaction data, but without relying on distributed consensus. Instead, its construction process transfers trust from widely-used, third-party services onto ZipperChains's correctness guarantees. ZipperChain blocks are built by a pipeline of specialized services deployed on a small number of nodes connected by a fast data center network. As a result, ZipperChain transaction throughput approaches network line speeds and block finality is on the order of 500 ms. Finally, ZipperChain infrastructure creates blocks centrally and so does not need a native token to incentivize a community of verifiers.
Mohammad Alvian Dharma Nararya, Shuri Mariasih Gietty, Himawan Aditya Pratama
Tulisan ini mengkaji secara kritis kemunculan gim Play-to-Earn (P2E) dalam kerangka teknologi Web3, dengan berargumen bahwa janji desentralisasi yang dibawa oleh blockchain dan Non-Fungible Token (NFT) justru mereproduksi, bahkan memperkuat, pola-pola eksploitasi kapitalisme tradisional. Model P2E merujuk pada sistem permainan digital yang memungkinkan pemain memperoleh keuntungan finansial dari aktivitas bermain melalui mekanisme ekonomi berbasis token kripto, di mana aset dalam gim memiliki nilai tukar di pasar digital. Sementara itu, blockchain merupakan teknologi pencatatan terdistribusi yang menyimpan data transaksi di banyak komputer (nodes) dan sering diklaim sebagai fondasi desentralisasi digital karena tidak bergantung pada otoritas tunggal. Melalui analisis terhadap infrastruktur Web3 dan studi kasus gim Axie Infinity (2018), tulisan ini menunjukkan bahwa sistem digital yang diklaim membebaskan pengguna dari kontrol terpusat justru memusatkan kekuasaan ekonomi dalam bentuk yang lebih terselubung. Dengan kerangka teori kapitalisme digital dan konsep false needs dari Herbert Marcuse, penelitian ini memperlihatkan bahwa ekonomi P2E mengubah aktivitas bermain menjadi bentuk kerja (playbor) dan menundukkan pemain pada pasar spekulatif yang menguntungkan pengembang dan pemilik modal. Di Asia Tenggara, tempat basis pemain P2E tetap besar meskipun gelembung pasarnya telah pecah, sistem ini mengeksploitasi kondisi sosial-ekonomi yang rentan dengan membingkai ketidakstabilan finansial sebagai peluang. Tulisan ini berargumen bahwa “desentralisasi” dalam Web3 merupakan bentuk sentralisasi terselubung melalui kontrol algoritmik, opasitas infrastruktur, dan privatisasi platform, menunjukkan bahwa Web3 dan gim P2E bukanlah alternatif pasca-kapitalis, melainkan fase baru dari kapitalisme digital yang mengomodifikasi permainan dan mendistribusikan risiko ke bawah sambil mengonsolidasikan keuntungan di atas.
The Paris Journal on AI & Digital Ethics Bootstrapping Trust across Web2 and Web3 Domains Using Publicly Verifiable Web Data Yuan Lu¹, Qiang Tang² Corresponding authors:luyuan@iscas.ac.cn • qiang.tang@sydney.edu.au Abstract Through […]
Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of task-specific scores, yet seldom ask whether the embeddings themselves remain a truthful, interpretable reflection of the evolving network. We formalize this requirement as representation integrity and derive a family of indexes that measure how closely embedding changes follow graph changes. Three synthetic scenarios, Gradual Merge, Abrupt Move, and Periodic Re-wiring, are used to screen forty-two candidate indexes. Based on which we recommend one index that passes all of our theoretical and empirical tests. In particular, this validated metric consistently ranks the provably stable UASE and IPP models highest. We then use this index to do a comparative study on representation integrity of common dynamic graph learning models. This study exposes the scenario-specific strengths of neural methods, and shows a strong positive rank correlation with one-step link-prediction AUC. The proposed integrity framework, therefore, offers a task-agnostic and interpretable evaluation tool for dynamic-graph representation quality, providing more explicit guidance for model selection and future architecture design.
Anton Ivashkevich, Matija Piškorec, Claudio J. Tessone
We describe a prototype of a fully capable Ethereum Proof-of-Work (PoW) blockchain network running on multiple Raspberry Pi (RPi) computers. The prototype is easy to set up and is intended to function as a completely standalone system, using a local WiFi router for connectivity. It features LCD screens for visualization of the local state of blockchain ledgers on each RPi, making it ideal for educational purposes and to demonstrate fundamental blockchain concepts to a wide audience. For example, a functioning PoW consensus is easily visible from the LCD screens, as well as consensus degradation which might arise from various factors, including peer-to-peer topology and communication latency - all parameters which can be configured from the central web-based interface.
Vision Transformers achieve strong performance across computer vision tasks but suffer from quadratic computational complexity with respect to token count, limiting deployment in resource-constrained environments. Existing token pruning methods rely on attention scores to identify important tokens, but attention mechanisms capture query-specific relevance rather than intrinsic information content, potentially discarding tokens that carry information for subsequent layers or different downstream tasks. We propose fractal-guided token pruning, a method that leverages the correlation dimension Dcorr of token embeddings as a task-agnostic measure of geometric complexity. Our key insight is that tokens with high Dcorr span higher-dimensional manifolds in representation space, indicating complex patterns, while tokens with low Dcorr collapse to simpler structures representing redundant information. By computing a local Dcorr for each token and pruning those with the lowest values, our method retains geometrically complex tokens independent of attention-based relevance. The correlation dimension quantifies how token embeddings fill the representation space: embeddings from uniform background regions cluster tightly in low-dimensional subspaces (low Dcorr), while embeddings from complex textures or object boundaries spread across higher-dimensional manifolds (high Dcorr), reflecting their richer information content. Experiments on CIFAR-10 and CIFAR-100 with fine-tuned ViT-B/16 models show that fractal-guided pruning consistently outperforms random and norm-based pruning across all tested ratios. At forty percent pruning, fractal pruning maintains 92.26% accuracy on CIFAR-10 with only a 0.99 percentage point drop from the 93.25% baseline while achieving 1.17× speedup. Our approach provides a geometry-based criterion for token importance that complements attention-based methods and shows promising generalization between CIFAR-10 and CIFAR-100 datasets.
Regional banks emerged around the 1960s with the mission of contributing to the development and integration of Latin America, primarily through the financing of infrastructure projects, essential to the region's industrialization and trade flows.In 2000, the South American Regional Integration Initiative (IIRSA) was created, under whose Secretariat the Inter-American Development Bank (IDB), the Development Bank of Latin America (CAF), and FONPLATA -Development Bank -began working together to promote territorial planning and find financing solutions.Even with the dissolution of IIRSA, resulting from the paralysis of the Union of South American Nations (UNASUR) starting in 2017, the coordinated action of the three banks continued, through initiatives such as the Alliance for the Integration and Development of Latin America and the Caribbean (ILAT), the Sucre Declaration, and the "South American Integration Routes," demonstrating the resilience of infrastructure integration in the face of political change.Therefore, the overall objective of this thesis was to analyze the contribution of the development banks IDB, CAF, and FONPLATA to building regional infrastructure integration in South America.The methodology involved identifying integration models and operational concepts under which these banks operate; identifying the specific problems of Latin American regional infrastructure and its financing; and analyzing the performance of IDB, CAF, and FONPLATA both individually, focusing on documents, projects, and institutional structures focused on integration, and collectively, from the emergence of IIRSA to the Integration Routes.It was found that, despite the current general crisis in Latin American regionalism, both intellectual and institutional, the three banks are at the center of building a governance system for financing regional infrastructure in South America.However, they have shifted from a model centralized in IIRSA to one, after the end of this Initiative, focused on decentralized cooperation.
The Nepali government has declared all cryptocurrency-related activities illegal due to its exclusive currency issuance authority and stringent foreign exchange regulations. This prohibition is based on robust anti-money laundering laws and the potential applicability of evolving digital legislation. The article also assesses the costs and risks associated with illicit cryptocurrency activities and encompassing severe legal consequences, financial exposure, and cyber security threats. Finally, it explores the paradox of Nepal Rastra Bank's exploration into a Central Bank Digital Currency (CBDC), suggesting a recognition of digital currency's future while prioritizing national control and stability. The study concludes by emphasizing the imperative for public adherence to existing prohibitions while acknowledging the long-term trajectory towards digital financial innovation.
The global decentralized identity market is undergoing exponential transformation as organizations worldwide shift toward secure, user-centric identity management frameworks. Valued at USD 1.52 billion in 2024, the market is projected to reach USD 56.83 billion by 2030, expanding at an extraordinary CAGR of 82.9% from 2025 to 2030. This manuscript provides an in-depth analysis of decentralized identity systems that utilize blockchain and distributed ledger technologies to eliminate central points of failure, enhance user privacy, and safeguard digital interactions. It further explores the major drivers influencing market growth, including rising identity fraud, rapid digitalization, proliferation of cloud platforms, and implementation of strict regulatory data protection standards. Despite high initial investments posing a challenge for small and medium enterprises, increasing integration of artificial intelligence and machine learning in decentralized identity frameworks offers promising future opportunities. The study concludes with key segmental insights, regional dynamics, competitive landscape, and future implications for the decentralized identity ecosystem.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"—the process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.
The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.
Smart contracts enable programmatic agreements but face two persistent problems: high on-chain cost (throughput/latency) and weak privacy (public ledger exposes transaction semantics). We propose a hybrid on-chain/off-chain commitment scheme (HOC-C) that combines lightweight on-chain commitments, verifiable off-chain computation, and succinct zero-knowledge proofs to deliver privacy-preserving contract execution at scale. In HOC-C, sensitive inputs and heavy computations are executed off-chain by a consortium of replicated verifiers; the verifiers publish a succinct zk-SNARK proof of correct execution plus a small state commitment on-chain. The on-chain contract verifies the proof and updates state atomically. To prevent malicious collusion among verifiers, HOC-C integrates an economic incentive layer and challenge windows where anyone can publish refutation proofs; the refutation burden is designed to be less than the honest-verifier cost. We implement HOC-C using a prototype that plugs into an EVM-compatible chain (Ethereum testnet) and evaluate performance for representative workloads (private auctions, confidential supply-chain workflows, private token-transfer batching). The system reduces gas cost by an order of magnitude compared to naive on-chain execution while preserving end-to-end confidentiality for user inputs. We analyze security properties (soundness, liveness, and economic incentive compatibility) and discuss trade-offs: proof generation latency vs. throughput, verifier decentralization vs. amortized cost. HOC-C offers a practical roadmap for adopting private, inexpensive smart contracts on mainstream blockchains.
Transitioning to renewable energy is thus a very important component of global efforts toward combating climate change, especially in emerging economies where energy demand is fast outpacing supply. Carbon markets have emerged as a vital financial mechanism for supporting renewable energy projects by enabling the trade of carbon credits. The following abstract discusses how carbon markets affect multi-dimensionally the financial flows of renewable energy in developing nations: attracting investment, reducing capital costs, driving technology innovation, and delivering decentralized energy. Through case studies from Kenya, India, and Brazil, the article illustrates how carbon markets have indeed served to mobilize such large-scale renewable projects as wind farms and solar installations that improve the lot of rural and underserved communities. Despite the promise of carbon markets, it still faces regulatory gaps, market volatility, high transaction costs, and limited participation from local stakeholders. This may spell out actionable solutions, such as the development of regional carbon trading systems, enhancement of voluntary carbon markets, blended finance models, and the integration of emerging economies into global carbon market initiatives within frameworks like those under the Paris Agreement. Carbon markets could have a real catalyzing role in the transition toward renewable energy, with accelerated rates of greenhouse gas emission reduction and sustainable development in emerging economies, if they are able to successfully address these tacked barriers.
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
تناولت هذه الدراسة الفقهية مسألة المتاجرة بالرموز غير القابلة للاستبدال (NFTs)، وهي رموز رقمية فريدة تُسجَّل على تقنية البلوك تشين وتُستخدم لإثبات ملكية الأصول الرقمية. وهدفت الدراسة إلى بيان الحكم الفقهي لهذه المعاملات في ضوء القواعد العامة للمعاملات المالية في الشريعة الإسلامية، من خلال تحليل خصائص هذه الرموز ومجالات استخدامها، وبيان مدى انطباق الضوابط الشرعية على تلك المعاملات. وقد خلصت الدراسة إلى أن الحكم يتوقف على طبيعة كل حالة، حيث إن بعض صور المتاجرة بهذه الرموز قد تندرج تحت البيوع الجائزة، إذا توفرت فيها شروط الصحة وانتفت عنها المحاذير الشرعية؛ بينما بعض الصور الأخرى قد تُعد من المعاملات المحرمة، بسبب الغرر أو الجهالة أو المقامرة. وأوصت الدراسة بضرورة وضع أطر شرعية واضحة لتنظيم هذه المعاملات في ظل التطورات الرقمية المتسارعة. This jurisprudential study explores the issue of trading in Non-Fungible Tokens (NFTs), which are unique digital assets registered on blockchain technology and used to prove ownership of digital content. The study aims to determine the Islamic legal ruling on such transactions in light of the general principles of financial dealings in Islamic law, by analyzing the features of NFTs, their uses, and the extent to which they comply with Shariah standards. The study concludes that the ruling depends on the nature of each case. Some forms of NFT trading may be considered permissible sales if the necessary conditions are met and no Shariah violations are involved. However, other forms may be deemed prohibited due to uncertainty, ambiguity, or elements of gambling.