This study provides a comprehensive analysis of the dynamic interconnectedness between traditional fiat currencies (CHF and JPY) and cryptocurrencies (Bitcoin and Ethereum) across three distinct periods: the pre-COVID-19, the COVID-19 pandemic, and the Russia-Ukraine conflict. Our methodology employs the Quantile Vector Autoregressive (QVAR) connectivity approach, beginning with the average median and progressively extending to various quantiles over time revealing both short-term and long-term dynamic connectedness. Our findings reveal that Bitcoin and Ethereum exhibit significant interconnectedness and predominantly act as net transmitters of volatility, especially in the short term. In contrast, CHF and JPY generally serve as shock absorbers, showing strong self-dependency and conditional safe-haven properties. Particularly, the Swiss Franc occasionally transmits volatility during extreme market conditions, highlighting its dynamic role. The implications of our study are crucial for investors and portfolio managers aiming to adjust dynamically their portfolios by actively monitoring market trends to modify their allocations between traditional safe-haven currencies and cryptocurrencies. Specifically, in times of increased volatility, managers should temporarily reduce exposure to cryptocurrencies and increase allocations in stable fiat currencies such as CHF and JPY. Conversely, during more stable periods, higher investments in cryptocurrencies could yield better returns. implementing a real-time volatility monitoring system can aid managers in making well-informed choices to optimize risk management strategies. Dynamic hedging is preferred over static approaches.
In this paper, we describe the motivation, design, security properties, and a prototype implementation of NickPay, a new privacy-preserving yet auditable payment system built on top of the Ethereum blockchain platform. NickPay offers a strong level of privacy to participants and prevents successive payment transfers from being linked to their actual owners. It is providing the transparency that blockchains ensure and at the same time, preserving the possibility for a trusted authority to access sensitive information, e.g., for audit purposes or compliance with financial regulations. NickPay builds upon the Nicknames for Group Signatures (NGS) scheme, a new signing system based on dynamic ``nicknames'' for signers that extends the schemes of group signatures and signatures with flexible public keys. NGS enables identified group members to expose their flexible public keys, thus allowing direct and natural applications such as auditable private payment systems, NickPay being a blockchain-based prototype of these.
In this paper, we present the first large-scale empirical study of smart contract dependencies, analyzing over 41 million contracts and 11 billion interactions on Ethereum up to December 2024. Our results yield four key insights: (1) 59% of contract transactions involve multiple contracts (median of 4 per transaction in 2024) indicating potential smart contract dependency risks; (2) the ecosystem exhibits extreme centralization, with just 11 (0.001%) deployers controlling 20.5 million (50%) of alive contracts, with major risks related to factory contracts and deployer privileges; (3) three most depended-upon contracts are mutable, meaning large parts of the ecosystem rely on contracts that can be altered at any time, which is a significant risk, (4) actual smart contract protocol dependencies are significantly more complex than officially documented, undermining Ethereum's transparency ethos, and creating unnecessary attack surface. Our work provides the first large-scale empirical foundation for understanding smart contract dependency risks, offering crucial insights for developers, users, and security researchers in the blockchain space.
Bu çalışma, kripto para birimlerinin nedensellik ilişkilerini doğrusal olmayan yöntemlerle inceleyerek, bu varlıkların birbirleriyle olan etkileşimlerini daha kapsamlı bir şekilde anlamayı amaçlamaktadır. Çalışmada, 2020'nin ilk haftasından 2022'nin otuz birinci haftasına kadar olan sekiz önemli kripto varlığının (Bitcoin, Ethereum, Tether, USD Coin, Binance Coin, Ripple ve Cardano) haftalık dolar cinsinden döviz kuru verileri kullanılmıştır. Veri seti 135 gözlemi içermektedir. Çalışma, özellikle durağanlık analizi ve doğrusal olmayan nedensellik analizi olmak üzere ekonometrik zaman serisi ve yapay sinir ağı (YSA) analiz yöntemlerini kullanmaktadır. Değişkenlerin durağanlık kararları, üç birim kök testine dayanmaktadır. Bunlar; ADF Testi, PP Testi ve KPSS Testleridir. Değişkenler arasındaki ilişki, Doğrusal olmayan Granger Nedensellik Analizi kullanılarak keşfedilmiştir. Tüm analizler R-Studio programında gerçekleştirilmiştir. Durağanlık analizinde, USDT ve USDC'nin düzeyde (I (0)) durağan olduğu, diğer değişkenlerin ise birinci farkta (I (1)) durağan olduğu belirlenmiştir. Çalışma sonucunda, hiçbir değişken arasında doğrusal olmayan nedensellik ilişkisine rastlanmamıştır.
Asif Ahmad Bhat, Rizal Mohd Nor, Md Amiruzzaman, Md. Rajibul Islam · 5 authors
Blockchain, such as Bitcoin and Ethereum, has received significant attention and widespread usage in recent years. However, blockchain scalability has emerged as a challenging issue. This article explores the existing scalability options for blockchain, which can be categorized into two groups: first layer solutions and second layer solutions. First layer solutions involve network modifications like altering block size, while second layer solutions encompass techniques applied outside of the blockchain. Ethereum, the second largest blockchain, utilizes the Ethereum Virtual Machine (EVM) for executing smart contracts on the blockchain. Currently, there are several EVM-compatible blockchains with noticeable differences. In this study, we evaluated multiple platforms for conducting business processes in trade finance. We considered both Layer 1 and Layer 2 blockchain solutions and examined variations in cost and performance (speed). Based on the evidence gathered in this study, we provide recommendations for system designers to consider when selecting a blockchain platform.
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causal–noncausal invertible–noninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMA–GARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewness—common in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (α) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
UTokyo Repositoryは本学で生産されたさまざまな学術成果を電子的形態で集中的に蓄積・保存し、世界に発信することを目的としたインターネット上の発信拠点です。 The UTokyo Repository is the system to store and provide digital resources created by members of the University of Tokyo. Its main purpose is to develop digital collections, make them available online, and preserve them for long-term access.
Electronic Health Records (EHRs) are now a necessary component of contemporary healthcare, but managing them presents a number of security, privacy, and interoperability issues. In order to solve these issues, this study introduces a unique framework for EHR management that combines four cutting-edge technologies: blockchain, Zero-Knowledge Proofs (ZKP), Ciphertext-Policy Attribute-Based Encryption (CP-ABE), and InterPlanetary File System (IPFS). Our solution makes use of the Ethereum blockchain for transparent and safe record-keeping, IPFS for efficient and decentralized data storage, CP-ABE for fine-grained access control, and ZKP for private authentication. We offer computational proofs for important components together with a thorough security analysis utilizing formal verification tools like ProVerif and Tamarin Prover. Comparing our framework to other alternatives, the findings show that it provides stronger security guarantees, better privacy protection, and increased scalability. Our approach also defends against a broader variety of possible threats, such as man-inthe-middle attack, repudiation attacks, and side-channel attacks. This work opens the door for more effective and patient-cantered healthcare information systems by advancing secure and privacy-preserving EHR management.
The nature of virtual assets and their legal regulation is a challenge for policymakers, because virtual assets themselves are a new phenomenon in the field of social and economic relations, which is significantly different from established types of property. The market of virtual assets, which has achieved significant development over the past 10 years, is of interest for research and from a fiscal point of view, because despite its significant volume, agreed approaches to the taxation of operations carried out in such a market are absent or are at the stage of development. A significant number of new challenges facing the legislator when determining the tax regime of operations with virtual assets arise from their qualities, which are categorically different from other types of assets. Virtual assets have a significant number of subspecies, which on the one hand are significantly different from each other, and on the other hand share common features. In particular, the most famous virtual assets - Bitcoin, Ethereum are completely decentralized, do not have a specific issuer, do not certify any civil rights of the owner, and do not have security. On the other hand, such types of virtual assets as electronic money tokens («stablecoins») or tokens related to assets are a form of expression of civil rights, namely the rights of claim against the issuer. Thus, it is problematic to determine which set of characteristics to use to distinguish virtual assets from other types of property while taking into account the full range of diversity of virtual assets themselves. In addition, transactions with virtual assets take place in forms different from transactions with cash, securities, etc. The ability of subjects to store, exchange, acquire and alienate virtual assets without the participation of any financial institutions or other intermediaries is another challenge in rulemaking, because it complicates the application of existing control methods in the field of taxation. A separate category of problems is also the phenomenon of decentralized finance («DeFi»), which eliminates intermediaries not only from the basic operations of moving virtual assets, but also from more complex economic operations, such as credit activities, loans, collateral, derivative contracts, etc. Considering the above, the relevance of the research lies in the emergence of qualitatively new categories of social relations, which, like any other social and economic relations, require legal regulation. Currently available regulatory instruments are not able to fully cover all the variety of operations with virtual assets, and to provide appropriate, special regulation of them.
• Agent-based modeling can be used to study the sociotechnical dynamics associated with technology implementation. • Ethereum’s ERC-721protocol can be leveraged to facilitate reducing the prevalence of counterfeit electronic parts. • Widespread adoption of blockchain is required to reduce the flow of counterfeit electronic parts. • Adoption is sensitive to the direct and indirect cost associated with blockchain implementation. • Integerating blockchain with business practice verification can reduces its cost, leading to an increase in adoption. Safety-critical, mission-critical, and infrastructure-critical systems (e.g., aerospace, transportation, defense, and power generation) are forced to source parts over exceptionally long periods of time from a supply chain that they do not control. Such systems are exposed to the dual risks of the impacts of system failure and the exposure to an unauthorized electronics marketplace over decades. Therefore, critical systems operators, manufacturers, and sustainers, must implement policies and technologies to reduce the risk of obtaining counterfeit parts. Blockchain technology, as a distributed ledger platform, has shown promise for resolving the issues associated with a lack of trust, transparency in peer-to-peer transactional networks, and compromised supply chains. There are opportunities to apply blockchain for supply chain concepts to mitigate the risks associated with part authenticity in the electronic part supply chain. This paper introduces a supply-chain blockchain framework resilient to aging (e.g., the loss of involvement of the original component manufacture and its authorized distributors, and loss of part transaction history). An agent-based model is introduced as a novel platform to test the impact of the proposed blockchain framework on supply-chain parties as well as the prevalence of counterfeits in the electronics supply chain. The model can validate the proposed protocol over the entire life cycle of a part (i.e., from active production to discontinuance and beyond) and predict the parties’ adoption rates, and changes in the prevalence of counterfeit parts. Application of the model to a public participation blockchain based on Ethereum ERC- 721 protocols indicates that the participation level of independent distributors directly affects the efficacy of blockchain in the prevention of transactions containing counterfeit parts. A proposed certification-based blockchain participation approach can be effective if certifications require large enough test accuracy limits and high previous owner certification thresholds.
Paula Ungureanu, Francesca Bellesia, Carlotta Cochis
This study investigates an emblematic case of innovation failure in blockchains as to understand how turbulent episodes of innovation failure shape the socio-technical organization of digital ecosystems. The Decentralized Autonomous Organization ( The DAO ) was an alternative model of organizational governance based on the Ethereum blockchain which registered one of the biggest successes in crowdfunding history and fell victim to one of the biggest hacks of the crypto world. Our empirical qualitative study combines interviews, archival and social media data to develop a grounded theory on how innovation failure was framed and dealt with in the Ethereum ecosystem. Our findings highlight the key role of blaming processes following innovation failures in digital ecosystems. Building on blame theory, we theorize about the interplay between human and technological blaming, and document a process called multi-distributed blaming whereby actors circle between multiple blames to an ecosystem's human and technological components, with multi-level (i.e., organizational and technological) consequences for the ecosystem. By adopting a socio-technical perspective, our findings contribute to blame theories, to the literature on digital ecosystems and to the scant research on blockchain organization. • We study The DAO blockchain experiment as a case of failure in digital ecosystems • We show the interplay between organizational and technological ecosystems’ elements • We introduce a multi-distributed blaming process in complex digital ecosystems • We show how blaming processes shape the consequences of an innovation failure • We show the consequences for the ecosystem’s organizational and technological players
Kode Lakshmi Durga Sindhujasri, Kaduputla Manogna, Sutrayeth Hari Yuktha Nanda, Baligiri Thandava Krishna · 5 authors
Abstract: Elections play a fundamental role in any democratic system, and ensuring their integrity is of utmost importance. Traditional voting methods, such as paper ballots and Electronic Voting Machines (EVMs), suffer from various limitations, including security vulnerabilities, vote tampering, low voter turnout, delays in result processing, and a lack of transparency. Digital voting solutions offer convenience but raise concerns regarding data security and susceptibility to cyber threats. Blockchain technology presents a promising solution to these challenges by providing a decentralized, transparent, and tamperproof framework for conducting elections. As a distributed ledger system, blockchain records transactions in an immutable and verifiable manner, ensuring the integrity of votes. Key features such as decentralization, cryptographic security, transparency, and anonymity make blockchain a robust choice for secure e-voting. In this paper, we propose and implement a blockchainbased e-voting system using Ethereum smart contracts and Web3.js. Our system enforces single-use voting credentials, preventing duplicate votes, and leverages gas fees to mitigate fraudulent voting attempts. Additionally, we develop a web-based application that demonstrates the practical implementation of blockchain voting, discussing its advantages, challenges, and limitations in real-world scenarios
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Abstract Blockchain-based emerging technologies such as decentralized finance (DeFi), cryptocurrencies, tokens, and smart contracts have introduced innovative frameworks for resource allocation and economic interactions. Ethereum, as the major technical network foundation of DeFi and tokenized assets, is becoming increasingly pivotal in facilitating an extension and alternative to traditional finance for many stakeholders, including those who are “unbanked”. Moreover, the recent transition of Ethereum from a proof-of-work (PoW) mechanism to a proof-of-stake (PoS) consensus mechanism and the Shanghai upgrade may significantly impact Ether (ETH) distribution. However, the status quo and dynamics of wealth distribution, especially after these changes in governance structure, remain unclear. By utilizing a rich dataset spanning the entire Ethereum history from July 2015 to December 2024, we analyze the balances across address groups of different sizes and the role of key economic activities and infrastructure components within Ethereum, such as exchanges, DeFi platforms, and staking. To provide detailed insights into ETH’s distributional equality, our approach combines descriptive, longitudinal, and causal inference analyses; a complete enumeration of more than 98 million unique wallet addresses; and novel on-chain analysis. Our findings show a substantial concentration of ETH within a small fraction of addresses, with approximately 0.3% of wallets holding nearly 95% of the total supply, despite the majority of wallets holding less than 0.1% ETH. However, the ETH distribution broadly resembles wealth distributions in traditional economies, with a log-normal body and Pareto-like tails. We assert that previous studies have overstated the concentration of ETH. Additionally, our dynamic analysis reveals a nuanced trend toward less concentration over time, driven by market cycles, increasing staking participation, and reinvestment in DeFi. These results challenge the notion of pervasive centralization. This study contributes to a deeper understanding of the current ETH distribution and its evolution over time. Therefore, this work provides an objective, data-driven basis for the ongoing discussion on wealth (in)equality in blockchain-based ecosystems, particularly in DeFi.
The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates code features with transactional data to enhance reputability prediction. Our framework initially focuses on AI-based code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining code and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.
Blockchain technology is rapidly evolving, with scalability remaining one of its most significant challenges. While various solutions have been proposed and continue to be developed, it is essential to consider the blockchain trilemma -- balancing scalability, security, and decentralization -- when designing new approaches. One promising solution is the zero-knowledge proof (ZKP)-based rollup, implemented on top of Ethereum. However, the performance of these systems is often limited by the efficiency of the ZKP mechanism. This paper explores the performance of ZKP-based rollups, focusing on a solution built using the Hardhat Ethereum development environment. Through detailed analysis, the paper identifies and examines key bottlenecks within the ZKP system, providing insight into potential areas for optimization to enhance scalability and overall system performance.
Zeta Avarikioti, Eleftherios Kokoris Kogias, Ray Neiheiser, Christos Stefo
The security of many Proof-of-Stake (PoS) payment systems relies on quorum-based State Machine Replication (SMR) protocols. While classical analyses assume purely Byzantine faults, real-world systems must tolerate both arbitrary failures and strategic, profit-driven validators. We therefore study quorum-based SMR under a hybrid model with honest, Byzantine, and rational participants. We first establish the fundamental limitations of traditional consensus mechanisms, proving two impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators collectively exceed $1/3$ of the participants; and (2) even under synchronous network assumptions, SMR remains unattainable if this coalition comprises more than $2/3$ of the validator set. Assuming a synchrony bound $Δ$, we show how to extend any quorum-based SMR protocol to tolerate up to $1/3$ Byzantine and $1/3$ rational validators by modifying only its finalization rule. Our approach enforces a necessary bound on the total transaction volume finalized within any time window $Δ$ and introduces the \emph{strongest chain rule}, which enables efficient finalization of transactions when a supermajority of honest participants provably supports execution. Empirical analysis of Ethereum and Cosmos demonstrates validator participation exceeding the required $5/6$ threshold in over $99%$ of blocks, supporting the practicality of our design. Finally, we present a recovery mechanism that restores safety and liveness after consistency violations, even with up to $5/9$ Byzantine stake and $1/9$ rational stake, guaranteeing full reimbursement of provable client losses.
The rapid expansion of blockchain technology has led to increased security challenges, particularly in detecting fraudulent transactions and malicious activities within decentralized networks. Traditional anomaly detection techniques, including rule-based heuristics and supervised learning models, struggle to adapt to the dynamic and complex nature of blockchain transactions. This paper introduces a graph neural network (GNN)-based anomaly detection framework designed to improve blockchain security by leveraging the inherent graph structure of transaction networks. The proposed approach models blockchain transactions as a directed graph, where nodes represent wallet addresses and edges correspond to transaction flows. By applying spatial and temporal graph learning techniques, the framework captures both network topology and transaction evolution over time, allowing for the identification of anomalous activities such as money laundering, phishing scams, and Ponzi schemes. The GNN model incorporates graph convolutional networks (GCN), graph attention networks (GAT), and gated recurrent units (GRU) to learn both spatial dependencies and sequential patterns within blockchain transactions. Experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the GNN-based framework outperforms conventional fraud detection methods in terms of precision, recall, and false positive reduction. The model successfully detects fraudulent transactions with an F1-score of 0.92, showing its effectiveness in identifying emerging threats in blockchain networks. These results highlight the potential of deep learning-based anomaly detection in enhancing blockchain security, providing a scalable and adaptive solution for detecting fraud in decentralized financial ecosystems.
In recent years, blockchains have been attracting attention because they are decentralized networks with transparency and trustworthiness. Generally, transactions on blockchain networks with higher transaction fees are processed preferentially compared to others. The processing fee varies significantly depending on other transactions; it is difficult to predict the fee, and it may be significantly high. These are major barriers to blockchain utilization. Although several consensus algorithms have been proposed to solve these problems, their performance has not been fully evaluated. In this study, we model a blockchain system with a base fee, such as in Ethereum, via a priority queueing model. To assess the model’s performance, we derive the stability condition, stationary probability, average number of customers, and average waiting time for each type of customer. In deriving the stability conditions, we propose a method that uses the theoretical values of the partial models. These theoretical values match well with those obtained from Monte Carlo simulations, confirming the validity of the analysis.
Hina Binte Haq, Syed Taha Ali, A. G. Sal'Man, Patrick McCorry · 5 authors
The Bitcoin mempool plays an integral role in transaction processing and propagation through the network. Frequent transaction congestion events, as well as spam and dust attacks can clog the mempool, leading to dropped transactions, processing delays, and increased transaction fees. Moreover, increasing transaction loads on the network result in higher resource costs to operate full nodes, thereby restricting Bitcoin's network footprint and negatively impacting its overall health and performance. In this paper, we present Carbyne, a novel mempool optimization scheme, which uses counting bloom filter constructions to adapt to increased transaction flows, thereby making nodes resilient to congestion and spam and dust attacks. We implement Carbyne in C++ and benchmark its performance using a novel data set of Bitcoin mempool activity over a 90-day period. We dramatically reduced the mempool's memory consumption by up to two orders of magnitude (from 300 MB to 3 MB) while verifying and forwarding transactions with 99.9% fidelity and a slight increase in computational load. We simulate extensive spam attacks on Carbyne and demonstrate that mempool loads of 1 GB can be accommodated in as little as 10 MB. Carbyne does not necessitate a hard fork, it will help deploy high-functioning nodes on resource-constrained platforms, and it may also be adapted to other cryptocurrencies.
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.
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.
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.
The decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can exploit. Detecting suspicious behaviors in account on the Ethereum blockchain can help mitigate attacks, including phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents. The proposed system introduces an ensemble stacking model combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN) to detect potential threats within the Ethereum platform. The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools-SHAP, LIME, and ELI5-provide interpretable feature insights, improving transparency in model predictions. The dataset used comprises 9,841 Ethereum transactions across 52 initial fields (reduced to 17 relevant features), encompassing both legitimate and fraudulent records. The experimental findings demonstrate that the proposed model achieves a superior accuracy of 99.6%, outperforming that of other cutting-edge methods. These findings demonstrate that the XAI-enabled ensemble stacking model offers a highly effective, interpretable solution for blockchain security, strengthening trust and reliability within the Ethereum ecosystem.
Juan Beccuti, Thunj Chantramonklasri, Matthias Hafner, Nicolas Oderbolz
This paper examines how various categories of Ethereum stakers respond to changes in the consensus issuance schedule, and the potential impact of such changes on the composition of the staking market. To this end, we have develop and calibrate a game-theoretic model of the Ethereum staking market, incorporating strategic interactions between various staking agents. Our findings suggest that solo stakers may be more sensitive to variations in staking rewards than ETH holders using centralized exchanges or liquid staking providers. This increased sensitivity is driven not only by the cost structure of solo staking, but also by the competitive dynamics between different staking solutions in the market. When faced with a downward-sloping issuance schedule, staking agents compete for limited staking yields, and their choice of staking supply affects the revenues of other stakers. Therefore, the presence of other staking methods with access to MEV revenues and other DeFi yield sources when staking, as well as inattentive stakers, puts competitive pressure on solo stakers. Consequently, our model predicts that a reduction in issuance is likely to crowd out solo stakers. We present preliminary empirical evidence to support this result, using an instrumental variable estimation approach to estimate the yield elasticity of staking supply for different staking categories.