Lambard Maxence, Cyrille Bertelle, D apos Amours Claude
In an increasingly complex contractual landscape, the demand for transparency, security, and efficiency has intensified. Blockchain technology, with its decentralized and immutable nature, addresses these challenges by reducing intermediary costs, minimizing fraud risks, and enhancing system compatibility. Smart contracts, initially conceptualized by Nick Szabo and later implemented on the Ethereum blockchain, automate and secure contractual clauses, offering a robust solution for various industries. However, their complexity and the requirement for advanced programming skills present significant barriers to widespread adoption. This study introduces a multi-level finite state machine model designed to represent and track the execution of smart contracts. Our model aims to simplify smart contract development by providing a formalized framework that abstracts underlying technical complexities, making it accessible to professionals without deep technical expertise. The hierarchical structure of the multi-level finite state machine enhances contract modularity and traceability, facilitating detailed representation and evaluation of functional properties. The paper explores the potential of this multi-level approach, reviewing existing methodologies and tools, and detailing the smart contract generation process with an emphasis on reusable components and modularity. We also conduct a security analysis to evaluate potential vulnerabilities in our model, ensuring the robustness and reliability of the generated smart contracts.
Muhammad Zubair Mumtaz, Zachary A. Smith, Naoyuki Yoshino
This study examines the nexus between money demand and cryptocurrencies by estimating two simultaneous equations using Divisia indices as a proxy for money demand and volume traded for cryptocurrencies. The study examines the linkage between cryptocurrencies and money demand and their potential influence over monetary policy actions. It finds that the volume of cryptocurrencies traded negatively influences money demand. Moreover, we see a positive association between money demand and cryptocurrencies, implying that as the demand for money increases, the demand for cryptocurrencies increases. Further, we examine the determinates of cryptocurrencies and report that the return of cryptocurrencies, the financial development index, GDP, inflation, and stock market indices are significant predictors of the demand for cryptocurrencies.
Hasib Ahmed Md Khyrul Islam, Huy T. Vo, Aditya Rane
In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational constraints of XR platforms while adhering to the stringent privacy requirements in sensitive settings. Experimental evaluations on multiple benchmark deepfake datasets demonstrate that TrustDefender achieves 95.3% detection accuracy, coupled with efficient proof generation underpinned by rigorous cryptography, ensuring seamless integration with high-performance artificial intelligence (AI) systems. By fusing advanced computer vision models with provable security mechanisms, our work establishes a foundation for reliable AI in immersive and privacy-sensitive applications.
Open access
2 source records
Adversarial Robustness in Machine Learning
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Green bonds have rapidly emerged as a transformative financial instrument within sustainable finance, channelling capital toward projects with explicit environmental benefits such as renewable energy, clean infrastructure, and climate adaptation. This paper provides a comprehensive investigation into the growth trajectory and impact of green bonds on sustainable finance, synthesizing evidence from empirical studies, systematic literature reviews, and industry analyses. The findings reveal that green bonds significantly enhance access to funding for environmentally friendly investments and contribute to market stability and transparency by requiring clear reporting and third-party verification of environmental outcomes. The issuance of green bonds is positively associated with factors such as renewable energy capacity and economic growth, while higher interest rates and market saturation in emission reductions can temper issuance growth. Sovereign green bonds, in particular, act as catalysts, fostering the expansion and quality of private green bond markets by setting benchmarks and improving green verification standards. Despite their promise, challenges persist, including risks of green washing and the lack of globally consistent certification standards. The integration of green bonds with innovative technologies such as decentralized finance (DeFi) is also explored as a means to further democratize and enhance the efficiency of sustainable finance. This research offers actionable insights for investors, policymakers, and academics seeking to leverage green bonds for the global transition to a green economy
The integration of artificial intelligence and human decision-making within blockchain systems has raised complex ethical considerations, necessitating the development of comprehensive theoretical frameworks. This research develops a multi-paradigm ethical framework addressing the ethical dimensions of hybrid intelligence—the dynamic interplay between human judgment and artificial intelligence—in the governance of blockchain technology and cryptocurrency systems. Drawing upon complexity theory and institutional theory, this study employs a theory synthesis methodology to investigate inherent paradoxes within hybrid intelligence systems, including how transparency creates new opacities in AI decision-making, decentralization enables centralized control, and algorithmic efficiency undermines ethical sensitivity. Through PRISMA-compliant systematic literature analysis of 50 relevant publications and theoretical synthesis, this research demonstrates how blockchain technology fundamentally redefines hybrid intelligence by establishing novel forms of trust, accountability, and collective decision-making. The framework advances three testable propositions regarding emergent intelligence properties, adaptive capacity, and institutional legitimacy while providing practical governance principles and implementation methodologies for blockchain developers, regulators, and participants. This study contributes theoretically by bridging the fields of complex systems and institutional analysis, integrating complex adaptive systems with institutional legitimacy processes through a multi-paradigm integration methodology. It delivers an ethical framework that addresses accountability distribution in Decentralized Autonomous Organizations, quantifies ethical challenges across major platforms, and offers empirically validated guidelines for balancing algorithmic autonomy with human oversight in decentralized systems.
Open access
2 source records
Blockchain Technology Applications and Security
Neuroethics, Human Enhancement, Biomedical Innovations
Eyasu Getahun Chekole, Howard Halim, Jianying Zhou
Unauthorized access remains one of the critical security challenges in the realm of cybersecurity. With the increasing sophistication of attack techniques, the threat of unauthorized access is no longer confined to the conventional ones, such as exploiting weak access control policies. Instead, advanced exploitation strategies, such as session hijacking-based attacks, are becoming increasingly prevalent, posing serious security concerns. Session hijacking enables attackers to take over an already established session between legitimate peers in a stealthy manner, thereby gaining unauthorized access to private resources. Unfortunately, traditional access control mechanisms, such as static access control policies, are insufficient to prevent session hijacking or other advanced exploitation techniques. In this work, we propose a new multi-factor authorization (MFAz) scheme that proactively mitigates unauthorized access attempts both conventional and advanced unauthorized access attacks. The proposed scheme employs fine-grained access control rules (ARs) and verification points (VPs) that are systematically generated from historically granted accesses as the first and second authorization factors, respectively. As a proof-of-concept, we implement the scheme using different techniques. We leverage bloom filter to achieve runtime and storage efficiency, and blockchain to make authorization decisions in a temper-proof and decentralized manner. To the best of our knowledge, this is the first formal introduction of a multi-factor authorization scheme, which is orthogonal to the multi-factor authentication (MFA) schemes. The effectiveness of our proposed scheme is experimentally evaluated using a smart-city testbed involving different devices with varying computational capacities. The experimental results reveal high effectiveness of the scheme both in security and performance guarantees.
Alejandro Cuevas, Manoel Horta Ribeiro, Nicolas Christin
Online content creators spend significant time and effort building their user base through a long, often arduous process that requires finding the right "niche" to cater to. So, what incentive is there for an established content creator known for cat memes to completely reinvent their channel and start promoting cryptocurrency services or covering electoral news events? We explore this problem of repurposed channels, whereby a channel changes its identity and contents. We first characterize a market for "second-hand" social media accounts, which recorded sales exceeding USD 1M during our 6-month observation period. Observing YouTube channels (re)sold over these 6 months, we find that a substantial number (53%) are used to disseminate policy-sensitive content, often without facing any penalty. Surprisingly, these channels seem to gain rather than lose subscribers. We estimate the prevalence of repurposing using two snapshots of ~1.4M YouTube accounts sampled from an ecologically valid proxy. In a 3-month period, we estimate that ~0.25% channels were repurposed. We experimentally confirm that these repurposed channels share several characteristics with sold channels -- mainly, they have a significantly high presence of policy-sensitive content. Across repurposed channels, we find channels similar to those used in influence operations, as well as channels used for financial scams. Repurposed channels have large audiences; across two observed samples, repurposed channels held ~193M and ~44M subscribers. We reason that purchasing an existing audience and the credibility associated with an established account is advantageous to financially- and ideologically-motivated adversaries. This phenomenon is not exclusive to YouTube and we posit that the market for cultivating organic audiences is set to grow, particularly if it remains unchallenged by mitigations, technical or otherwise.
Fine-tuning the large language models (LLMs) are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning (FL) supports data privacy, the central server requirement creates a single point of attack and vulnerability to poisoning attacks. Generalizing the result in this direction to 70B-parameter models in the heterogeneous, trustless environments has turned out to be a huge, yet unbroken bottleneck. This paper introduces FLock, a decentralized framework for secure and efficient collaborative LLM fine-tuning. Integrating a blockchain-based trust layer with economic incentives, FLock replaces the central aggregator with a secure, auditable protocol for cooperation among untrusted parties. We present the first empirical validation of fine-tuning a 70B LLM in a secure, multi-domain, decentralized setting. Our experiments show the FLock framework defends against backdoor poisoning attacks that compromise standard FL optimizers and fosters synergistic knowledge transfer. The resulting models show a >68% reduction in adversarial attack success rates. The global model also demonstrates superior cross-domain generalization, outperforming models trained in isolation on their own specialized data.
Driven by blockchain technology, numerous industries are increasingly adopting smart contracts to enhance efficiency, reduce costs, and improve transparency. As a result, ensuring the security of smart contracts has become critical. Traditional detection methods often suffer from low efficiency, are prone to missing complex vulnerabilities, and have limited accuracy. Although deep learning approaches address some of these challenges, issues with both accuracy and efficiency remain in current solutions. To overcome these limitations, this paper proposes a symmetry-inspired solution that harmonizes bidirectional and generative semantic patterns. First, we generate distinct feature extraction segments for different vulnerabilities. We then use the Bidirectional Encoder Representations from Transformers (BERT) module to extract original semantic features from these segments and the Generative Pre-trained Transformer (GPT) module to extract generative semantic features. Finally, the two sets of semantic features are fused using a multi-attention mechanism and input into a classifier for result prediction. Our method was tested on three datasets, achieving F1 scores of 93.33%, 93.65%, and 92.31%, respectively. The results demonstrate that our approach outperforms most existing methods in smart contract detection.
This study investigates behavioral factors that shape the intention to reinvest in cryptocurrency among young investors in Jakarta, Indonesia. The research adopts a conceptual framework based on the Theory of Planned Behavior (TPB) and the Theory of Interpersonal Behavior (TIB), combining rational variables such as financial literacy and financial influencer with emotional variables including swift benefit and cognitive biases. A total of 528 valid responses were collected through an online survey and analyzed using PLS-SEM. The results indicate that positive sentiment (β = 0.477) and control belief (β = 0.331) have a significant impact on reinvestment intention. Emotional factors show stronger indirect effects through these mediators compared to rational factors. In addition, perceived technological advancement plays a moderating role by significantly enhancing the effect of control belief on reinvestment intention (β = 0.208), while reducing the influence of positive sentiment (β = -0.458). These findings suggest that emotional responses are more dominant than rational evaluations in guiding reinvestment decisions in volatile digital markets. The integration of TPB and TIB provides a theoretical contribution to the field of behavioral finance and offers practical recommendations for improving investor literacy, platform engagement strategies, and regulatory support in the cryptocurrency ecosystem.
The introduction of smart contracts into the social sphere and their active use requires a detailed analysis. The classification of such contracts and the description of their features will make it possible to specify the legal regulation in the field of the use of these electronic systems. The purpose of the study is to examine the features of smart contracts and propose a more complete (expanded) classification of them for various reasons. The research is based on methods of comparative analysis, synthesis, interpretation of legal norms and a comprehensive analysis of works on the chosen topic by both domestic authors and foreign specialists. The work resulted in additional grounds on which smart contracts can be categorized. The characteristics of smart contracts are also described: efficiency, security, lack of centralization, transparency, peer-to-peer, automation, and protection against fraud. Conclusion: smart contracts can be further classified depending on the environment in which they are executed (the blockchain technologies used), depending on their retribution for the parties to the transaction.
Introduction. The rapid development of technology is significantly transforming all spheres of human activity, and the financial industry is no exception. Recent decades have been marked by the emergence and rapid spread of blockchain technologies, which promise to revolutionize traditional approaches to doing business. From decentralized finance (DeFi) to smart contracts and asset tokenization, blockchain opens up unprecedented opportunities to increase transparency, security, efficiency, and reduce operational costs. Purpose: a comprehensive analysis of the prospects and challenges of applying blockchain technologies in the financial activities of enterprises, as well as substantiation of their role in increasing the efficiency, transparency, and security of corporate finances in the modern digital economy. Methods. To achieve the goal, our research will be based on the integrated application of a number of scientific methods. Analysis and synthesis will become the foundation for an in-depth study of existing scientific papers, reports and analytical materials related to the implementation of blockchain technologies in the financial sphere. Through analysis, we can break down complex concepts into components, and synthesis will help to combine the data into a single, holistic picture. A systems approach will allow us to consider the financial activities of enterprises integrating blockchain as a complex interconnected system, assessing the impact of the technology on various aspects of business operations and identifying potential synergies and risks. Results. In this scientific article, the conducted research deeply delves into the scope of application of blockchain technologies in the financial activities of enterprises, revealing both their significant transformational potential and significant challenges on the path to implementation. Conclusions: The application of blockchain technologies in the financial activities of enterprises has enormous potential for the transformation and optimization of many processes. From increased transparency and security to automation and access to new sources of funding, the benefits are clear. However, successful blockchain integration requires careful analysis, overcoming regulatory and technical challenges, and significant investment in skills development.
Eleonóra Bassi, Michael Lustenberger, Srebrenka Letina
This research examines the structure of blockchain-based voluntary carbon market (VCM) and the factors shaping their formation. Conducted as part of the 2023–2025 Innosuisse project 104.664 IP-EE, it aims to provide insights to support participants in strategic positioning within the network. To our knowledge, this is one of the first empirical attempts to map the blockchain-enabled VCM ecosystem with social-network analysis, thereby extending digital-transition research into the climate-finance domain. Specifically, the study focuses on three exploratory aims: identifying the network position of key participants, evaluating the influence of blockchain platform affiliation on collaboration, and analyzing the relationship between standardization methods and network positioning. Using network analysis, the study categorizes participants like project owners, certification bodies, blockchain platforms, and carbon credit marketplace into distinct roles such as key hubs, strategic bridges, local connectors, and peripheral nodes. Participants using the same blockchain platform exhibit a moderate clustering tendency, suggesting shared infrastructure plays a role in fostering partnerships. Additionally, the choice of standardization methods for carbon credits correlates with specific network positions. These findings offer a structure-based view of how technical design choices may redistribute influence across the market–an issue of growing interest as regulators and standards bodies debate digital registry architectures. By uncovering these dynamics, the study emphasizes the importance of strategic positioning within blockchain-based VCMs. Native tokenization strategies are shown to simplify supply chains, while the decentralized ecosystem fosters diverse approaches to collaboration. The conceptual framework may be transferable to other emerging green-finance networks, providing a springboard for comparative and longitudinal analyses.
Smart contracts are trustworthy, immutable, and automatically executed programs on the blockchain. Their execution requires the Gas mechanism to ensure efficiency and fairness. However, due to non-optimal coding practices, many contracts contain Gas waste patterns that need to be optimized. Existing solutions mostly rely on manual discovery, which is inefficient, costly to maintain, and difficult to scale. Recent research uses large language models (LLMs) to explore new Gas waste patterns. However, it struggles to remain compatible with existing patterns, often produces redundant patterns, and requires manual validation/rewriting. To address this gap, we present GasAgent, the first multi-agent system for smart contract Gas optimization that combines compatibility with existing patterns and automated discovery/validation of new patterns, enabling end-to-end optimization. GasAgent consists of four specialized agents, Seeker, Innovator, Executor, and Manager, that collaborate in a closed loop to identify, validate, and apply Gas-saving improvements. Experiments on 100 verified real-world contracts demonstrate that GasAgent successfully optimizes 82 contracts, achieving an average deployment Gas savings of 9.97%. In addition, our evaluation confirms its compatibility with existing tools and validates the effectiveness of each module through ablation studies. To assess broader usability, we further evaluate 500 contracts generated by five representative LLMs across 10 categories and find that GasAgent optimizes 79.8% of them, with deployment Gas savings ranging from 4.79% to 13.93%, showing its usability as the optimization layer for LLM-assisted smart contract development.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
Axie Infinity is a blockchain-based video game offering players the chance to earn crypto tokens in exchange for their time spent playing the game. During the COVID-19 lockdowns, the game's popularity surged alongside the crypto market and stories of early adopters’ quick returns on investments circulated among online crypto and Web3 communities. As the game's rapidly growing userbase plateaued, the community experienced several growth-related crises, one of which saw the value of the game's tokens crash. But players were not passive victims of these developments. They responded by creating a “scholarship” program to secure the flow of new players to the platform and actively commented on their commitment to the “grind” of playing the game to recoup their investments. This article treats the trajectory of Axie Infinity as both an exemplar case study of broader dynamics in the crypto gaming landscape—a process we call the economization of play —and as a unique site in which players were not simply duped by the promise of the game, but were responding to crises proactively with risk mitigating and rationalizing strategies.
Persistent financial frictions - including price volatility, constrained credit access, and supply chain inefficiencies - have long hindered productivity and welfare in the global agricultural sector. This paper provides a theoretical and applied analysis of how fiat-collateralized stablecoins, a class of digital currency pegged to a stable asset like the U.S. dollar, can address these long-standing challenges. We develop a farm-level profit maximization model incorporating transaction costs and credit constraints to demonstrate how stablecoins can enhance economic outcomes by (1) reducing the costs and risks of cross-border trade, (2) improving the efficiency and transparency of supply chain finance through smart contracts, and (3) expanding access to credit for smallholder farmers. We analyze key use cases, including parametric insurance and trade finance, while also considering the significant hurdles to adoption, such as regulatory uncertainty and the digital divide. The paper concludes that while not a panacea, stablecoins represent a significant financial technology with the potential to catalyze a paradigm shift in agricultural economics, warranting further empirical investigation and policy support.
The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26,204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management.
Collaboration among multiple large language model (LLM) agents is a promising approach to overcome inherent limitations of single-agent systems, such as hallucinations and single points of failure. As LLM agents are increasingly deployed on open blockchain platforms, multi-agent systems capable of tolerating malicious (Byzantine) agents have become essential. Recent Byzantine-robust multi-agent systems typically rely on leader-driven coordination, which suffers from two major drawbacks. First, they are inherently vulnerable to targeted attacks against the leader. If consecutive leaders behave maliciously, the system repeatedly fails to achieve consensus, forcing new consensus rounds, which is particularly costly given the high latency of LLM invocations. Second, an underperforming proposal from the leader can be accepted as the final answer even when higher-quality alternatives are available, as existing methods finalize the leader's proposal once it receives a quorum of votes. To address these issues, we propose DecentLLMs, a novel decentralized consensus approach for multi-agent LLM systems, where worker agents generate answers concurrently and evaluator agents independently score and rank these answers to select the best available one. This decentralized architecture enables faster consensus despite the presence of Byzantine agents and consistently selects higher-quality answers through Byzantine-robust aggregation techniques. Experimental results demonstrate that DecentLLMs effectively tolerates Byzantine agents and significantly improves the quality of selected answers.
With rapid advancements in quantum computing, it is widely anticipated that scalable quantum hardware may threaten classical cryptography and hence, the internet and the current information security infrastructure in the coming decade. This is mainly due to the operational realizations of quantum algorithms such as Grover and Shor, to which the current classical encryption protocols are vulnerable. Blockchains, i.e., blockchain data structures and their data, rely heavily on classical cryptography. One approach to secure blockchains is to attempt to achieve conceptual information-theoretic security under certain assumptions by defining blockchains on quantum technologies. There have been two major conceptualizations of blockchains data structures on quantum registers: the time-entangled Greenberger-Horne-Zeilinger (GHZ) state blockchain and the quantum hypergraph blockchain. We conceptualize a new quantum blockchain framework combining features of both these schemes to achieve the conceptual information-theoretic protection against undetected measurement attack (physics-based disturbance detectability) of the time-entangled GHZ blockchain and the scalability and efficiency of the quantum hypergraph blockchain in the proposed quantum blockchain data structure and framework. In this work, we propose a novel quantum blockchain architecture that integrates temporal GHZ entanglement with phase encoding inspired by the quantum hypergraph blockchain. The proposed design combines the conceptual information-theoretic tamper sensitivity/resistance of temporal entanglement with improved encoding efficiency, offering a unified conceptual framework for scalable and secure quantum blockchains.
Junliang Luo, Katrin Tinn, Şengül Duran, Di Wu · 5 authors
Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing instruments issued across multi-chain Web3 infrastructures, with growing significance for transparency, accessibility, and financial inclusion. While the market has expanded rapidly, empirical analyses of transaction-level behaviours remain limited. This paper conducts a quantitative, function-level dissection of U.S. Treasury-backed RWA tokens, including BUIDL, BENJI, and USDY across multi-chain: mostly Ethereum and Layer-2s. Decoded contract calls expose core financial primitives such as issuance, redemption, transfer, and bridging, revealing patterns that distinguish institutional participants from smaller or retail users for the extent and limits of inclusivity in current RWA adoption. To infer address-level economic roles, we introduce a curvature-aware representation learning model. Our method outperforms baseline models in role inference on our collected U.S. Treasury transaction dataset and generalizes to address classification across broader public blockchain transaction datasets. The decoded transaction-level patterns in tokenized U.S. Treasuries across chains surface the degree of retail participation, and the role inference model enables the distinction between institutional treasuries, arbitrage bots, and retail traders based on behavioral patterns, facilitating future more transparent, inclusive, and accountable Web3 finance.
As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design.