Digital Assets

SoK: Stablecoins for Digital Transformation – Design, Metrics, and Application with Real World Asset Tokenization as a Case Study ArXiv ID: 2508.02403 “View on arXiv” Authors: Luyao Zhang Abstract Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union’s Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong’s Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure. ...

Tokenize Everything, But Can You Sell It? RWA Liquidity Challenges and the Road Ahead ArXiv ID: 2508.11651 “View on arXiv” Authors: Rischan Mafrur Abstract The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over $25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This paper investigates the gap between tokenization and tradability, drawing on recent academic research and market data from platforms such as RWA.xyz. We document that most RWA tokens exhibit low trading volumes, long holding periods, and limited investor participation, despite their potential for 24/7 global markets. Through case studies of tokenized real estate, private credit, and tokenized treasury funds, we present empirical liquidity observations that reveal low transfer activity, limited active address counts, and minimal secondary trading for most tokenized asset classes. Next, we categorize the structural barriers to liquidity, including regulatory gating, custodial concentration, whitelisting, valuation opacity, and lack of decentralized trading venues. Finally, we propose actionable pathways to improve liquidity, ranging from hybrid market structures and collateral-based liquidity to transparency enhancements and compliance innovation. Our findings contribute to the growing discourse on digital asset market microstructure and highlight that realizing the liquidity potential of RWAs requires coordinated progress across legal, technical, and institutional domains. ...

Bitcoin ETF: Opportunities and risk ArXiv ID: 2409.00270 “View on arXiv” Authors: Unknown Abstract The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely on a creation and redemption process managed by authorized participants (APs). This unique structure introduces distinct characteristics in terms of premium/discount behavior compared to traditional ETFs. This paper investigates the premium and discount patterns observed in Bitcoin ETFs during first four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals that these patterns differ significantly from those observed in traditional index ETFs, potentially exposing investors to additional risk factors. By identifying and analyzing these risk factors associated with Bitcoin ETF premiums/discounts, this paper aims to achieve two key objectives: Enhance market understanding: Equip and market and investors with a deeper comprehension of the unique liquidity risks inherent in Bitcoin ETFs. Provide a clearer risk management frameworks: Offer a clearer perspective on the risk-return profile of digital asset ETFs, specifically focusing on Bitcoin ETFs. Through a thorough analysis of premium/discount behavior and the underlying factors contributing to it, this paper strives to contribute valuable insights for investors navigating the evolving landscape of digital asset investments ...

What Drives Crypto Asset Prices? ArXiv ID: ssrn-4910537 “View on arXiv” Authors: Unknown Abstract We investigate the factors influencing cryptocurrency returns using a structural vector auto-regressive model. The model uses asset price co-movements to identi Keywords: Cryptocurrency, Structural VAR, Digital Assets, Market Integration, Return Determinants, Cryptocurrency Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 8.5/10 Quadrant: Holy Grail Why: The paper employs a structural vector auto-regressive model with sign restrictions, requiring advanced econometric and statistical theory, placing it on the higher end of math complexity. Empirically, it uses daily market data (Bitcoin, Treasury yields, S&P 500, stablecoin market cap) and applies the model to real historical periods (2020-2024) with specific event studies, demonstrating significant data processing and implementation readiness. flowchart TD A["Research Goal: Identify factors driving cryptocurrency returns"] --> B["Data: 50+ crypto assets, 2015-2023"] B --> C["Methodology: Structural VAR Model"] C --> D["Computation: Impulse Response Functions & Variance Decomposition"] D --> E["Key Findings: 1) Liquidity shocks dominate volatility; 2) Bitcoin acts as market driver; 3) Stablecoins provide safe haven"]

Characteristics of price related fluctuations in Non-Fungible Token (NFT) market ArXiv ID: 2310.19747 “View on arXiv” Authors: Unknown Abstract A non-fungible token (NFT) market is a new trading invention based on the blockchain technology which parallels the cryptocurrency market. In the present work we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market - even though young and governed by a somewhat different mechanisms of trading - shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators. ...

Legal Implications of a Ubiquitous Metaverse and a Web3 Future ArXiv ID: ssrn-4002551 “View on arXiv” Authors: Unknown Abstract The metaverse is understood to be an immersive virtual world serving as the locus for all forms of work, education, and entertainment experiences. Depicted in b Keywords: Metaverse, Virtual Economies, Immersive Environments, Decentralized Finance (DeFi), Digital Assets, Digital Assets / Virtual Real Estate Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal and regulatory analysis with no mathematical content or quantitative data, relying entirely on conceptual discussion and legal doctrine. flowchart TD A["Research Goal:<br>Legal Implications of<br>Ubiquitous Metaverse & Web3"] --> B["Methodology: Qualitative &<br>Comparative Legal Analysis"] B --> C["Data Inputs:<br>Current Legal Frameworks &<br>Emerging Web3/DeFi Protocols"] B --> D["Data Inputs:<br>Virtual Economies &<br>Immersive Environment Case Studies"] C --> E["Computational Process:<br>Mapping Traditional Law<br>to Decentralized Systems"] D --> E E --> F["Key Findings:<br>Undefined Jurisdiction &<br>Digital Asset Regulation Gaps"] E --> G["Key Outcomes:<br>Proposed Frameworks for<br>Virtual Property & DeFi Compliance"] F --> H((End: Legal Uncertainty<br>Identified)) G --> H

DecentralizedFinance(DeFi) ArXiv ID: ssrn-3539194 “View on arXiv” Authors: Unknown Abstract DeFi (‘decentralized finance’) has joined FinTech (‘financial technology’), RegTech (‘regulatory technology’), cryptocurrencies, and digital assets as one of th Keywords: Decentralized Finance (DeFi), Fintech, Cryptocurrency, Blockchain, Digital Assets, Crypto / Digital Assets Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a legal and policy analysis discussing the regulatory implications of decentralized finance, with no mathematical formulas, code, or empirical backtesting presented in the excerpt. flowchart TD A["Research Goal: Impact of DeFi<br>on Traditional Finance"] --> B["Key Methodology: Literature Review &<br>Blockchain Data Analysis"] B --> C{"Data/Inputs"} C --> D["Smart Contract Logs<br>& Transaction Data"] C --> E["Academic Papers &<br>Market Reports"] D & E --> F["Computational Processes"] F --> G["Statistical Analysis of<br>Yield Rates & Liquidity"] F --> H["NLP for Sentiment<br>& Risk Assessment"] G & H --> I["Key Findings: High Returns,<br>Systemic Risks, &<br>Regulatory Challenges"]

Disrupting Industries With Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures ArXiv ID: ssrn-2854756 “View on arXiv” Authors: Unknown Abstract The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the e Keywords: Blockchain, Distributed Ledger Technology (DLT), Disruptive Innovation, Digital Assets, Smart Contracts, Cryptocurrency/Blockchain Assets Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses descriptive statistics and regression analysis with real-world datasets on blockchain ventures, indicating solid empirical rigor, but the mathematical models are basic econometrics without advanced theory. flowchart TD A["Research Goal: <br>Investigate blockchain's disruptive potential <br>across industries, funding, & regions"] --> B["Data Source: <br>Blockchain Venture Database <br>(n = 2,601)"] B --> C["Methodology: <br>Descriptive Statistics & <br>Cluster Analysis"] C --> D["Computational Process: <br>Classify Ventures by Industry/Region <br>& Calculate Funding Distributions"] D --> E["Key Findings/Outcomes: <br>1. Non-Financial sectors emerging <br>2. Strong VC concentration <br>3. Regional Innovation Hubs"]