Blockchain Papers

Hitesh Kumar Sahu, Ipsita Parida, Avishek Bhandari, Nihar Ranjan Jena · 5 authors

No abstract is available for this record.

Amer A. Hijazi, Ali Alashwal, Milad Baghalzadeh Shishehgarkhaneh, Rodrigo N. Calheiros

. The integration of Blockchain with Building Information Modelling (BIM) addresses persistent challenges of transparency, accountability, interoperability, and trust in construction. Yet, systematic insights into Blockchain–BIM implementation remain limited. This study conducts a Systematic Literature Review (SLR), identifying six lifecycle stages where integration occurs, supported by 29 workflows and 30 technical methods. Applications include blockchain-secured design reviews, provenance tracking, automated procurement, smart payments, and immutable handover records. BIM data types—design metadata, cost and schedule data, IoT evidence, and compliance records—are mapped to smart contract functions and blockchain platforms. Key mechanisms such as hybrid on/off-chain storage, cryptographic hashing, access controls, and watermarking are comparatively analyzed. Results show permissioned platforms (e.g., Hyperledger Fabric) enable controlled collaboration, while public ones (e.g., Ethereum) support transparency and tokenization. The review provides a structured taxonomy and conceptual framework to advance theory and guide Blockchain–BIM adoption in complex construction projects.

Ruisheng Shi, Yuxuan Liang, Zijun Guo, Qin Wang · 7 authors

Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin (SP'15, SP'20, CCS'21, SP'23) and Monero (NDSS'25), but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings. We present the first end-to-end implementation of an eclipse attack targeting Ethereum (2.0 version) execution-layer nodes. Our attack exploits the bootstrapping and peer management logic of Ethereum to fully isolate a node upon restart. We introduce a multi-stage strategy that majorly includes (i) poisoning the node's discovery table via unsolicited messages, (ii) infiltrating Ethereum's DNS-based peerlist by identifying and manipulating the official DNS crawler, and (iii) hijacking idle incoming connection slots across the network to block benign connections. Our DNS list poisoning is the first in the cryptocurrency context and requires only 28 IP addresses over 100 days. Slots hijacking raises outgoing redirection success from 45\% to 95\%. We validate our approach through controlled experiments on Ethereum's Sepolia testnet and broad measurements on the mainnet. Our findings demonstrate that over 80\% of public nodes do not leave sufficient idle capacity for effective slots occupation, highlighting the feasibility and severity of the threat. We further propose concrete countermeasures and responsibly disclosed all findings to Ethereum's security team.

Amira Zaimia, Jalel Eddine Hajlaoui, Mohamed Nazih Omri

No abstract is available for this record.

Nursultan Abdullaev, Rustam Ibragimov

Several works in the literature have focused on the analysis of key stylized facts of financial and cryptocurrency returns linked to fundamental problems of efficiency and predictability of financial and cryptocurrency markets, including heavy tails, absence of linear autocorrelations and volatility clustering. This paper provides a study of the above properties of Bitcoin and Ethereum markets using recently proposed robust, valid and statistically justified definitions of and methods for inference on market (in)efficiency, volatility clustering, and nonlinear dependence in return time series. In contrast to existing approaches, the inference methods used in the analysis are robust to heavy-tailedness, dependence and nonlinear dynamics of returns. The results of the study indicate that Bitcoin and Ethereum returns exhibit heavy tails, uncorrelatedness over time and volatility clustering largely similar to those in developed financial markets. The analysis has important implications for cryptocurrency pricing, market efficiency, econometric modeling, risk management, market participants and regulators.

Changhao Wu, Luyu Chen, Kai Wang, Weili Han · 5 authors

Occurring approximately once or twice in each block, sandwich attacks threaten Ethereum’s ecosystem by manipulating prices through strategically placed buy and sell transactions around pending user trades. Existing detection methods primarily rely on rigid heuristic rules, limiting their ability to detect increasingly sophisticated and dynamic attack variants, particularly those residing only in the mempool or spanning multiple blocks. In this paper, we propose SandWatch , a novel Ethereum sandwich attack detection framework that integrates a dual-task graph neural network (Dual-GNN) with heuristic methods. The framework comprises three main components. (1) An order-independent heuristic that captures fundamental token transfer patterns, reducing transaction volume by over 94% for subsequent graph analysis. (2) A Dual-GNN that simultaneously classifies sandwich attack transactions and DEX nodes, dynamically updating an address label pool to enhance accuracy and generalizability. (3) A positive-unlabeled learning strategy to leverage large-scale unlabeled data effectively. We first evaluate Dual-GNN on a benchmark dataset derived from publicly available Ethereum sandwich attack data, achieving an F1-score of 99.78%, outperforming the single-task baseline by 0.93 percentage points. We then deploy SandWatch on Ethereum transactions collected through blockchain interfaces and mempool pre-execution from January to May 2024. SandWatch detects 563,453 sandwich attacks, including 24,404 multi-attack, 4,902 cross-block, and 1,057 pool-failure variants, achieving an overall recall of 98.63% compared to the state-of-the-art benchmark platform. These results demonstrate the robustness of SandWatch in detecting sophisticated sandwich attacks within the real-world Ethereum ecosystem.

Zhixian Zhou, Bin Chen, Zhe Peng, Zhiming Liang · 7 authors

As 6G networks evolve, spectrum assets require flexible, dynamic, and efficient utilization, motivating blockchain based spectrum securitization. Existing approaches based on ERC404 style hybrid token models rely on frequent minting and burning during asset transfers, which disrupt token identity continuity and increase on chain overhead. This paper proposes the Semi Fungible Token Lock (SFT Lock) method, a lock/unlock based mechanism that preserves NFT identity and historical traceability while enabling fractional ownership and transferability. By replacing mint/burn operations with deterministic state transitions, SFT Lock ensures consistent lifecycle representation of spectrum assets and significantly reduces on chain operations. Based on this mechanism, a modular smart contract architecture is designed to support spectrum authorization, securitization, and sharing, and a staking mechanism is introduced to enhance asset liquidity. Experimental results on a private Ethereum network demonstrate that, compared with ERC404 style hybrid token models, the proposed method achieves substantial gas savings while maintaining functional correctness and traceability.

Jackie Zhanbiao Li, Jacie Jia Liu

Abstract With the introduction of spot Ethereum ETFs, Ethereum plays an increasingly important role in the cryptocurrency market. In this paper, we propose a Bayesian modelling framework incorporating a mixture copula for co-modelling Ethereum returns with Bitcoin or FTSE 100 returns. The mixture copula is designed as a combination of the Clayton copula and its three rotations, Frank, and Gaussian copulas. It provides substantial flexibility for handling a variety of dependency structures. The Bayesian approach offers the advantage of jointly estimating both the margins and copulas and simulating future returns in a coherent procedure. Using 10 different risk or risk-return measures, we provide updated empirical evidence on Ethereum’s role in both cryptocurrency and mixed portfolios. The analysis not only evaluates its diversification potential numerically but also sheds light on how the optimal allocations vary across distinct risk preferences and portfolio objectives. Moreover, based on the data of 2017–2024, we estimate that Ethereum futures has a hedging effectiveness on Bitcoin of about 30–40% across different risk preferences. Beyond these findings, the Bayesian mixture copula framework represents a methodological contribution to the modelling of complex dependence structures between financial returns. Taken together, our study delivers new insights that are particularly relevant in light of the evolving cryptocurrency landscape and the increasing integration of digital assets into mainstream investment practice.

Kwestan Ahmed Ismael, Heshu Othman Faqe, Mohammed Hussein Abdalla, Hindreen A. Taher

In this work we use historical market data from Bitget to predict weekly open prices of Ethereum (ETH) for a 96-week period with the Prophet forecast model trained by using Particle Swarm Optimization (PSO) algorithm. Because of this, the research delves into automated hyperparameter tuning for Prophet in order to improve forecast performance on cryptocurrency markets where volatility, structural breaks and irregular trading patterns pose a significant challenge to time series prediction. The PSO algorithm is a good method to explore the high dimensional parameter space in which it can strike between the global analysis and local exploitation for detecting minimal forecast errors. Based on evaluating model performance for which we used accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in training, test holdout & full-fit situations. PSO-optimized Prophet: The results show a great in-sample fitting and fast convergence behaviour, as the best CV RMSE is slightly higher than the lowest one should have obtained if used only 10 iterations. Although forecasts exhibit stability and track long-term trends well, the model does not predict short-term fluctuations in the holdout set with high accuracy (wider forecast uncertainty intervals). Our results shed light on the utility of PSO to improve Prophet-based price prediction in cryptocurrencies, reinforce the relevance of uncertainty quantification in asset markets and inform risk-aware decisions of financial agents dealing with unstable assets.

Dhanraj, Latha P, Kushal P Bhat, Nihal Prabhu · 5 authors

This study sets forth a blockchain-driven system for safe, traceable, and effective supply chain management in manufacturing. It combines Ethereum blockchain, AI-powered fraud detection, and real-time tracing. A CNN-LSTM hybrid model was found most useful for anomaly detection, supplemented by smart contracts for automatic monitoring. The system was constructed with a React front-end, SQLite, Web3, Solidity, PyTorch, and an external API for geographical mapping. The outcomes demonstrate improvements in fraud detection, transparency, and operational efficiency, highlighting the feasibility of a scalable, tamper-evident architecture for intelligent supply chain systems.

Jannik Albrecht, Ghassan Karame

While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.

R. Kaladevi, V. UmaRani, Modafar Ati, Shanmugasundaram Hariharan · 6 authors

Crowdfunding is a revolutionary finance business model and creative initiative in fundraising, but traditional finance models are facing problems such as high intermediary costs, insecurity, lack of transparency, and mishandling of money. This research paper proposes a decentralized crowdfunding mechanism based on blockchain technology, which ensures the fundraising process with automation, trust, and transparency. The self-triggered Ethereum smart contracts written in Solidity object-oriented programming language are used to develop the crowdfunding system, which is deployed and tested in the Remix IDE environment. Remix is linked to the Ganache local blockchain for checking fund transfers. This process automates the main crowdfunding operations like goal verification, fund collection, withdrawal, and donor refunds. The smart contract eliminates the need for middlemen in the centralized system. Also, each transaction is permanently available on the blockchain, guaranteeing traceability, auditability, and guarding against financial fraud via immutability. The results show that a decentralized crowdfunding mechanism with cheap operating costs, transparent execution, and safe fund transfers is feasible. The potential use of decentralized crowdfunding to improve stakeholder trust and operational effectiveness in digital fundraising ecosystems is exhibited in this work. Future research can expand this architecture to public testnets and incorporate sophisticated features like decentralized identity verification and token-based rewards.

Mimmo Parente, L. Rizzuti

Abstract Automatic trading systems cope with the needs of put out emotional biases from the trading operation of public assets. These systems place orders based on a price model that forecasts the future price of an asset. Those systems, developed by edge funds and institutional investors, are not available to the public, and extensive research in this field is worth the effort. In this research, we developed a short-term price model based on a neural network and used it to forecast the near-future price direction. More in depth, we introduced the feature extraction process and parametric labeling strategy to build an ML ready dataset that includes more than 400 cryptocurrencies. The model is then validated by building a trading strategy on the two most capitalized cryptos at the time of writing: Bitcoin and Ethereum. The validation uses a trading simulation that spans six years of historical data for Bitcoin and Ethereum, including both retrospective (backtest) and prospective (forward test) evaluations. The results demonstrate that the neural network-based model exhibits a very good generalization to patterns found in historical data, enabling predictions in future data within the trading simulation. In addition, a comprehensive analysis of the importance of features was conducted to enhance the interpretability and performance of the model. Finally, we test our model in a simulated trading session; it shows that, with a simple buy-only strategy plus a stop loss, the trading system limits the draw dawn during bear markets.

Awid Vaziry, Sandro Rodriguez Garzon, Christoph Wronka, Axel Küpper

Public blockchains provide no native mechanism to verify the legal identity behind a deployed smart contract, which blocks institutional adoption and compliance with EU regulations such as MiCA and AMLR. We present KYC Seal, the first protocol that extends the EU eIDAS trust infrastructure to Ethereum smart contracts by cryptographically binding them to Qualified Electronic Seals issued by Qualified Trust Service Providers (QTSPs). The protocol realizes the full eIDAS trust chain, from the European Commission's List of Trusted Lists through Member-State trusted lists and QTSP-signed X.509 certificates down to the individual smart contract, natively on-chain. An on-chain parser extracts identity fields directly from the QTSP-signed certificate bytes at registration. Both cryptographic verifications, the QTSP issuance signature and the certificate holder's seal signature, are performed once at registration and cached as on-chain state, reducing per-interaction seal verification to a pure state check. A new P-256 elliptic-curve precompile in Ethereum (deployed December 2025) makes these one-time cryptographic steps economical, enabling trustless on-chain verification of eIDAS identities without oracles or runtime intermediaries. A reference implementation, a formal security analysis, and a gas evaluation are the subject of forthcoming work.

Bastien Baude, Vincent Danos, Hamza El Khalloufi

We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization.

Ziyue Wang, Zongwen Shen, Lei Chen, Wei Song · 7 authors

Decentralized applications on EVM-compatible blockchains are powered by smart contracts—reprogrammable logic deployed on-chain. Among them, smart contract factories represent a distinct class of contracts that automate the creation of other contracts through CREATE / CREATE2 , enabling scalable and repeatable deployments of tokens, protocol modules, and NFT collections. Factories are now the dominant vehicle for contract deployment and mint the majority of contracts. Yet, the ecosystem still lacks (i) a global view of their prevalence and application domains, (ii) a catalog of implementation families beyond upgradeable proxies, and (iii) guidance on factory-specific security pitfalls that ripple across deployment pipelines. To address these gaps, we present the first ecosystem-scale measurement of factory activity on two EVM-compatible chains: Ethereum and Polygon. We implement a bytecode-based factory contract detector that disassembles 434 million deployed runtimes, builds control-flow graphs, and uses reachability analysis to confirm on-path CREATE / CREATE2 operations. This yields 120,204 Ethereum factories and 69,258 Polygon factories. The resulting dataset enables three key findings: (i) longitudinal measurements show factories have minted over 90% of contracts since 2020, concentrated in a small cadre of high-volume deployers; (ii) clustering 3,000 verified factories reveals four dominant application domains spanning Finance & token, Infrastructure & Protocol, Proxy & upgrade, and NFT & creator use cases; and (iii) semantic inspection distills six recurring implementation patterns alongside factory-specific attack vectors and security issues. Altogether, these results contribute to a deeper understanding of the current status, implementation patterns, and security issues of factory-based deployments.

Juraj Hric, Yiping Lin

This chapter starts with the introduction to blockchain , technology with a potential utility across finance and banking use cases. Then, we proceed and discuss bitcoin and further the notion of tokenization within the decentralized finance (DeFi) framework. Then, we expose the reader to practical examples of utilizing smart contracts from programmatic perspective. We use Ethereum platform with its own Solidity language to build a smart contract application using Python scripting sequences. The focus of this chapter is on the process of asset tokenization in order to build a foundational understanding to be applied at a later stage of the book. Finally, we provide a number of contra-arguments relevant to exposing deficiencies in blockchain-based technologies to be aware of from a practitioner’s standpoint.

Maheshwari Venkatasen, Prasanna Mani

The convergence of the real world with virtual and augmented reality, known as the “Metaverse,” is gaining momentum, threatening to upend multiple global industries. It's undeniable that people are incredibly interested in the Metaverse. Although Blockchain is still in its infancy, it is already vital to the growth of the digital economy. The blockchain technology that underpins cryptocurrencies and NFTs (NonFungible Tokens) is useful for tracking the supply and circulation of digital currencies and for governance, transparency, ease of access, and interoperability purposes. Given its infinite potential, the Metaverse has ushered in a period of rapid expansion across many of the economy's most important industries, real estate included. Metaverse platforms were being set up by people with sway in the blockchain or cryptocurrency industries so that they could acquire virtual real estate as NFTs, develop it, and stake it. Metaverse marketplaces create a virtual space using VR, Blockchain, and NFT technology, then sell access to that space to consumers in the form of NFTs. Even though many Metaverse services offer free accounts, cryptocurrency is required when buying or trading virtual assets on platforms that use the Blockchain. To buy and sell virtual assets on several blockchain-based platforms, such as Decentraland's MANA and Sandbox's SAND, Ethereum-based crypto tokens are required. Non-fungible tokens and cryptocurrencies are used by various blockchain-based platforms today, facilitating the development, acquisition, and monetization of distributed digital assets. As centralized data storage has many drawbacks, the Metaverse concept lacks Blockchain. Blockchain's global reach and decentralized nature as a digital source fundamentally set the Metaverse apart from the capabilities of the classic internet, which, of course, takes the form of websites and applications. Without needing a trusted third party or governing body, the blockchain-based Metaverse makes all internet data accessible. This chapter mainly focuses on blockchain-enabled Metaverse platforms, which are still developing augmented and virtual reality tools to enable user interaction with the environment.

Yitian Wang, Yebo Feng, Yingjiu Li, Jiahua Xu

In blockchain systems operating under the Proof-of-Stake (PoS) consensus mechanism, fairness in transaction processing is essential to preserving decentralization and maintaining user trust. However, with the emergence of Maximal Extractable Value (MEV), concerns about economic centralization and content manipulation have intensified. To address these vulnerabilities, the Ethereum community has introduced Proposer Builder Separation (PBS), which separates block construction from block proposal. Later, enshrined Proposer Builder Separation (ePBS) was also proposed in EIP-7732, which embeds PBS directly into the Ethereum consensus layer. Our work identifies key limitations of ePBS by developing a formal framework that combines mathematical analysis and agent-based simulations to evaluate its auction-based block-building mechanism, with particular emphasis on MEV dynamics. Our results reveal that, although ePBS redistributes responsibilities between builders and proposers, it significantly amplifies profit and content centralization: the Gini coefficient for profits rises from 0.1749 under standard PoS without ePBS to 0.8358 under ePBS. This sharp increase indicates that a small number of efficient builders capture most value via MEV-driven auctions. Moreover, 95.4% of the block value is rewarded to proposers in ePBS, revealing a strong economic bias despite their limited role in block assembly. These findings highlight that ePBS exacerbates incentives for builders to adopt aggressive MEV strategies, suggesting the need for future research into mechanism designs that better balance decentralization, fairness, and MEV mitigation.

Liyi Zeng, Wei Xu, Zhaoquan Gu, Yanchun Zhang

The growing popularity of blockchain technology has underscored the need for robust network security. However, public blockchain networks remain vulnerable to attacks in which adversaries exploit numerous nonfunctional peer connections to disrupt block propagation across the entire network. In this article, we propose a practical nontargeted delay attack method and validate its feasibility, scalability, and significant impacts on blockchain networks of varying sizes, including EthereumPoW (ETHW) and premerge Ethereum Mainnet. In the ETHW network with 95 nodes, our adversarial peers introduce delays ranging from 0.33 to 2.8 s for half the nodes, with nearly one-third experiencing delays exceeding 5.9 s, derived from the 90th percentile of delay times. When in the premerge Ethereum network with 5739 nodes, over 80% of peers experience prolonged block propagation, resulting in a 77% increase in delay time, underlining the attacks' scalability and efficacy in large-scale environments. We also optimize the Ethereum client Geth by relaxing certain connection restriction, significantly reducing attack costs. Delving deeper, we analyze the implications of delay attacks on proof-of-work (PoW) and proof-of-stake (PoS) consensus mechanisms, illustrating how attackers can gain extra revenues through such attacks. Specifically, we propose a novel combined strategy to facilitate reorganization attacks under PoS. These findings highlight the urgent need to strengthen network-layer defenses and reinforce peer-to-peer (P2P) network protocol security against real-world delay exploits.

Shengwei You, Aditya Joshi, Andrey Kuehlkamp, Jarek Nabrzyski

Cross-chain bridges constitute the single largest vector of systemic risk in Decentralized Finance (DeFi), accounting for over \$2.8 billion in losses since 2021. The fundamental vulnerability lies in the binary nature of existing bridge security models: a bridge is either fully operational or catastrophically compromised, with no intermediate state to contain partial failures. We present ASAS-BridgeAMM, a bridge-coupled automated market maker that introduces Contained Degradation: a formally specified operational state where the system gracefully degrades functionality in response to adversarial signals. By treating cross-chain message latency as a quantifiable execution risk, the protocol dynamically adjusts collateral haircuts, slippage bounds, and withdrawal limits. Across 18 months of historical replay on Ethereum and two auxiliary chains, ASAS-BridgeAMM reduces worst-case bridge-induced insolvency by 73% relative to baseline mint-and-burn architectures, while preserving 104.5% of transaction volume during stress periods. In rigorous adversarial simulations involving delayed finality, oracle manipulation, and liquidity griefing, the protocol maintains solvency with probability $>0.9999$ and bounds per-epoch bad debt to $<0.2%$ of total collateral. We provide a reference implementation in Solidity and formally prove safety (bounded debt), liveness (settlement completion), and manipulation resistance under a Byzantine relayer model.

Matteo Vaccargiu, Riccardo Lai, Maria Ilaria Lunesu, Andrea Pinna · 5 authors

We study how bots contribute to open-source discussions in the Ethereum ecosystem and whether they influence developers' emotional tone. Our dataset covers 36,875 accounts across ten repositories with 105 validated bots (0.28%). Human participation follows a U-shaped pattern, while bots engage in uniform (pull requests) or late-stage (issues) activity. Bots respond faster than humans in pull requests but play slower maintenance roles in issues. Using a model trained on 27 emotion categories, we find bots are more neutral, yet their interventions are followed by reduced neutrality in human comments, with shifts toward gratitude, admiration, and optimism and away from confusion. These findings indicate that even a small number of bots are associated with changes in both timing and emotional dynamics of developer communication.

Papa Ousseynou Diop, Chevallier Jm

The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets.

Junfeng Tian, Yuyang Zhao, Jin Kun Tian

No abstract is available for this record.