Blockchain Papers

Chiara Oldani, Giovanni S. F. Bruno, Marcello Signorelli

No abstract is available for this record.

Pyo Sujin, Huisu Jang

No abstract is available for this record.

Mikito Takayasu

Tokenization promises to convert lumpy, illiquid real-world assets into divisible, transferable claims, yet secondary markets for these instruments remain thin and trading is infrequent. Standard asset pricing models, including the capital asset pricing model and its liquidity-adjusted extensions, were not designed for assets whose holders derive consumption, access, or governance value directly from ownership. This paper develops a conceptual asset pricing framework for utility-backed non-fungible tokens (NFTs) and tokenized real-world assets by augmenting the liquidity-adjusted capital asset pricing model with a utility (convenience) yield. The framework decomposes the required pecuniary return into a risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover, and a utility-yield offset that lowers the return investors require in cash. Two analytical implications follow. First, utility backing compresses observed pecuniary returns without eliminating the underlying illiquidity premium. Second, where utility flows covary positively with illiquidity, estimates that regress pecuniary returns on liquidity proxies understate the gross illiquidity premium. An illustrative calibration, with parameter ranges drawn from the empirical tokenization literature, quantifies the mechanism rather than estimating it. The framework yields testable predictions and implications for valuation and disclosure.

Daniel Pereira Alves de Abreu, Octávio Valente Campos, Aureliano Angel Bressan

Objective: This study aims to evaluate the performance of different ARMA-GARCH model specifications in the risk management of major cryptocurrencies, investigating whether the inclusion of exogenous variables improves the calibration of risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). Methodology: To achieve this objective, 4,032 specifications of the ARMA-GARCH model applied to the ten main cryptocurrencies in trading were tested. The study incorporated the Fear and Greed Index and Bitcoin Trading Volume as exogenous variables in an ARMA-GARCH-X framework, comparing the performance of the different specifications against an ARMA(1,1)-GARCH(1,1) benchmark. Originality: Despite growing interest in crypto asset risk management, there are still gaps in the literature regarding the effectiveness of incorporating exogenous variables into forecasting models, as well as the increase in the quality of forecasts when using more complex models. Main results: The results indicate that the inclusion of external variables improves risk calibration in some assets, although the gains are marginal and heterogeneous. There is also no single optimal parameterization, requiring ARMA orders, GARCH specifications, and error distributions to be adjusted for each cryptocurrency. Theoretical/methodological contributions: From a methodological point of view, the study contributes by demonstrating the importance of specific calibration of ARMA-GARCH models for different cryptocurrencies in risk estimation. Furthermore, the results suggest that, although more complex models can improve tail risk estimation, the gains in predictive power over simpler models are limited. Keywords: Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.

Alfred Lehar, Christine A. Parlour

Blockchain-based trading venues, so-called decentralized exchanges, are at the heart of the decentralized finance revolution. Automated market makers, simple computer programs on the blockchain, administer liquidity and set the terms of trade. This article summarizes the key mechanisms behind these new markets, how they differ from traditional financial markets, how liquidity is provided, how prices are set, and how liquidity providers get compensated. We include a short guide on how to understand blockchain data and use these data for academic research.

SAMUEL OBOH, Boniface Dondo, S. Yakura Bassa, Gambo I. Bature

Ethereum, a leading digital asset by market value, has gained increasing attention from investors and researchers because of its high price volatility and market unpredictability. This study forecasts Ethereum cryptocurrency daily closing prices using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology, drawing on data from January 1, 2019, to December 31, 2025. Stationarity analysis via the Augmented Dickey-Fuller ADF and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests confirmed that first differencing was required to render the series suitable for the modeling. Through systematic model identification, estimation, and comparison of ten candidate ARIMA specifications, the ARIMA(1,1,0) model emerged as the optimal fit, yielding the lowest information criterion values of Akaike information criterion, Bayesian information criterion (AIC = 24,943.883; AICc = 24,943.84; BIC = 24,955.12). Residual diagnostic tests, including the Ljung-Box test for serial correlation, the Autoregressive Conditional Heteroskedasticity (ARCH-LM) test for heteroscedasticity, and the Shapiro-Wilk test for normality, confirmed that the model residuals are free of serial dependence, although they exhibit time-varying volatility and non-normal distribution, features commonly associated with financial time series. The fitted model was subsequently applied to generate 30-day ahead forecasts with 95% confidence intervals, revealing relatively stable price expectations in the near term alongside progressively widening prediction bands that reflect growing uncertainty over longer horizons. These findings underscore the practical utility of the parsimonious ARIMA(1,1,0) model as a transparent and accessible tool for short-term Ethereum-price forecasting and investment risk assessment.

Mikhail Perepelitsa

This paper presents an open-economy macroeconomic equilibrium model for Proof-of-Stake (PoS) networks with fee-burn mechanics (EIP-1559) that formalizes the strategic interplay between a Kelly-optimizing rational institutional investor and a utility-driven retail consumer. We analyze network dynamics across two behavioral regimes. In The Unbounded Accumulation Model, the consumer purely accumulates tokens, creating an exclusive buy-side pressure that interacts with institutional portfolio rebalancing to fuel an ever-expanding speculative bubble and generate compounding excess returns for investors. Conversely, in The Utility-Consumption Model, the consumer dynamically buys and sells tokens to balance crypto wealth against real-world fiat consumption. Within this framework, we derive an explicit steady-state equilibrium price for ETH, demonstrating how token valuation anchors to a stable fundamental baseline that scales directly with network adoption while completely dissolving the institutional yield premium. Our numerical simulations show that while exogenous traditional finance (TradFi) shocks propagate through portfolio rebalancing to drive high token price volatility, network inflation remains highly stable. Furthermore, we prove that network security is insulated from institutional monopoly by counter-cyclical consumer behavior. Our findings reveal that institutional excess wealth creation in PoS ecosystems is not native to the staking protocol itself, but is strictly driven by the leveraged extraction of the retail consumer's continuous demand for transactional utility.

Jianzheng Shi, Zhiyuan Wang, Ding Ding, Yue Wang · 7 authors

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of N=10 ETFs over T=514 trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment × BrandScore interaction is significant (β^=2.930, p=0.038), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects (p=0.012) and winsorization (p=0.004). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets.

Edward N. Udo, Goodness E. Mbakara

Abstract : This article investigates the efficacy of implementing an AI-powered automated trading system on the blockchain using advanced machine learning algorithms and smart contract technology. The work addresses the challenges of cryptocurrency market volatility, the need for real-time decision making and the limitations of traditional trading approaches that often result in suboptimal returns and exposure to increased risk. This work develops a comprehensive trading platform that combines Long Short-Term Memory (LSTM) neural networks, Q-Learning reinforcement learning algorithms and blockchain-based smart contracts to create an autonomous, intelligent trading system. The methodology follows a multi-layered approach that integrates real-time market data collection from CoinGecko and Snowtrace APIs, advanced AI model training using TensorFlow.js, and smart contract deployment on the Avalanche C-Chain using Hardhat and OpenZeppelin libraries. LSTM model is used for price prediction and Q-Learning agent is used for trading strategy optimization, while comprehensive risk management is implemented using Value at Risk (VaR) calculations, portfolio rebalancing algorithms and automated stop-loss mechanisms. The trading execution is facilitated through direct integration with Pangolin DEX smart contracts to ensure decentralized and trustless trade execution. The performance of the system is evaluated using a sophisticated backtesting engine with Monte Carlo simulations, comparing the AI-driven strategy against traditional buy-and-hold approaches. The performance metrics used were Sharpe ratio, maximum drawdown, win rate, and total return. The AI-powered token prediction system demonstrates a superior performance due to its ability to process complex, non-linear market patterns and adapt to changing market conditions through reinforcement learning, and execute trades with minimal latency through blockchain integration. The findings are expected to provide cryptocurrency traders and institutional investors with a robust and automated trading solution that leverages the benefits of both artificial intelligence and blockchain technology for improved investment outcomes and risk management.

Mercedes Esteban-Bravo, Jose M. Vidal-Sanz

This article studies the relationship between creator-related cues and market outcomes – price, time to sale, and non-sale – of non-fungible tokens (NFTs) in a leading marketplace. We first extract textual and visual indicators and summarize them into cognitive and affective composites using principal component analysis. We then estimate hedonic regression and duration models with creator-level random effects and recover creator-related components using empirical Bayes shrinkage. These components provide a descriptive decomposition of market outcomes into variation linked to observable asset cues and residual variation systematically associated with creators. We find substantial heterogeneity in creator-related components for both price and liquidity, while simple social-media metrics account for only a small share of that heterogeneity. We also model non-sale probability and show that creators’ social media activity is modestly associated with sale failure. Methodologically, the paper offers a transparent approach to mapping creator-related heterogeneity when creator metadata and standard brand-equity measures are limited.

Mustafa Berk Sacar, Sinem ATICI USTALAR, Selim ŞANLISOY

No abstract is available for this record.

Manel Mahjoubi, Jamel Eddine Henchiri

This paper investigates the impact of uncertainty on investor overconfidence in the Bitcoin market. While prior studies mainly focus on returns and volatility, limited attention has been paid to behavioral responses. Using a nonlinear autoregressive distributed lag (NARDL) model and monthly data from June 2011 to August 2022, we examine the asymmetric effects of major U.S. uncertainty indices (EPU, GPR, CPU, TEU and EURQ). The results reveal significant asymmetries. In the short run, increases in EPU and GPR reduce investor overconfidence, while decreases have the opposite effect. TEU and EURQ negatively affect investor confidence in both the short and long run. These findings highlight the key role of information-based uncertainty in shaping investor behavior and contribute to the behavioral finance literature by providing new evidence from cryptocurrency markets.

Christopher G. Harris

No abstract is available for this record.

Ariston Karagiorgis, Antonis Ballis

ABSTRACT This paper examines the relationship between skewness and kurtosis in Bitcoin spot and futures markets using high‐frequency data. We document a strong convex skewness–kurtosis relationship consistent with theoretical moment restrictions. Trading activity is positively associated with realized kurtosis, particularly in futures markets, though sensitive to specification and driven by extreme‐return episodes. Allowing the relationship to evolve over time reveals substantial curvature variation, indicating state‐dependent higher‐moment dynamics. The close co‐movement of results across markets suggests patterns reflect broad market‐wide conditions. The empirical framework is reduced‐form and results should be interpreted as conditional associations rather than causal effects.

Alfredo Medina Hernandez

Sovereign AI-Native Multi-Asset Trading, Execution, and Financial nfrastructure Charter Sovereign AI‑Native Financial Execution Infrastructure PARRALAX‑AIHFTFUND is a multi‑asset, AI‑native financial organism engineered to operate across traditional and blockchain‑based markets. It provides a unified execution layer where autonomous agents can observe markets, interpret structure, execute trades, manage risk, govern portfolios, issue digital assets, coordinate token economies, and maintain verifiable proof‑of‑computation. This repository contains the core infrastructure, protocol stack, and governance architecture for building sovereign, agent‑driven financial systems. Mission To build a sovereign AI‑native financial infrastructure capable of coordinating autonomous trading agents, multi‑asset execution, fund governance, risk control, digital‑asset creation, and market intelligence across both traditional and blockchain‑native markets. The system exists to move beyond bots, dashboards, and scripts. Its purpose is to become a real execution organism for financial markets. Vision PARRALAX‑AIHFTFUND aims to create a long‑horizon financial intelligence layer where AI agents can: Observe and interpret global market structure Execute trades across heterogeneous venues Manage risk and exposure Govern portfolios and internal policy Issue and manage digital assets Coordinate internal token economies Maintain proof‑of‑computation and decision lineage Operate across crypto, fiat, equities, FX, derivatives, AI tokens, NFTs, and future asset classes Build market memory over time The system is designed to evolve as markets evolve. Foundational Premise Modern markets are: Machine‑driven Fragmented Multi‑asset Tokenized Agent‑mediated A serious financial infrastructure must therefore operate across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets Autonomous agent economies High‑speed execution environments Governance‑controlled fund structures Programmable financial instruments PARRALAX‑AIHFTFUND is built to bridge old‑world and new‑world markets. What PARRALAX‑AIHFTFUND Is A sovereign trading infrastructure framework An AI‑native market execution system A multi‑asset financial operating layer A protocol stack for autonomous trading agents A fund governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine A compute‑receipt and proof‑trace system A foundation for future AI‑managed financial organisms It is built for real execution, not passive analysis. What PARRALAX‑AIHFTFUND Is Not Not a research repo Not a toy trading bot Not a simulation Not a dashboard Not a signal script collection Not a crypto hype project Not a single‑asset system Not a prediction‑only model Research supports the system. Research does not define the system. Status Active development. Core modules stabilizing. Execution layer expanding. Governance and digital‑asset subsystems in progress. PARRALAX‑AIHFTFUND is an AI‑native financial execution framework designed to coordinate autonomous agents across traditional and blockchain‑based markets. The system provides a unified operating layer for multi‑asset execution, risk management, fund governance, digital‑asset issuance, and verifiable compute‑traceability. System Mission To construct a sovereign financial intelligence layer capable of continuous operation across heterogeneous markets, enabling agents to observe market conditions, interpret structure, execute trades, manage exposure, and maintain internal governance. Operational Scope The system is engineered to function across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets and agent economies Governance‑controlled fund structures High‑speed execution environments Programmable financial instruments System Definition PARRALAX‑AIHFTFUND comprises: A sovereign trading and execution infrastructure A multi‑asset financial operating layer A protocol stack for autonomous trading agents A governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine with compute receipts Non‑Scope The system is not a research‑only repository, simulation toy, dashboard, signal script collection, or prediction‑only model. It is infrastructure‑first and execution‑oriented.

Ya-Wei Yang, Zih-Ying Lin, Yun-Chen Tang

This research examines 42 countries and investigates the relationship between geopolitical risk and global non-fungible token (NFT) investor attention. We use Google search volumes related to NFTs across different regions as a proxy for such attention. Our findings indicate that geopolitical risk positively impacts global NFT investor attention, suggesting that investors in countries with higher geopolitical risk may pay more attention to the NFT market. We further explore the effects across different NFT segments and find that geopolitical risk particularly influences investor attention in the metaverse segment. This positive nexus is further amplified during the Russia-Ukraine war and the COVID-19 pandemic.

Lennart Schwertfeger, Bodo Vogt

This paper demonstrates practical arbitrage trading on the cryptocurrency market. It provides guidance on how to build a high-frequency trading system that benefits from exhibiting arbitrage opportunities. It reveals the algorithm of the trading bot that incorporates the order placement and execution strategy between decentral and central cryptocurrency exchanges. The arbitrage algorithm is implemented on two different blockchains that interact with Uniswap and Balancer folks. Current research explores arbitrage opportunities with back-testing models, the paper focuses on trades with realized arbitrage trades. Practical arbitrage includes all operational costs, liquidity constraints, direct effects on markets, and competition with peer arbitrage traders.

Fernando Arruda de Faria

I replicate and extend the cross-sectional trend-factor methodology of Liebi, Stulz, and Tsyvinski (2024) on a contemporaneous out-of-sample period and a liquidity-restricted coin universe. Using 141 USDT spot pairs over 128 weekly observations from November 2023 to April 2026, an Elastic-Net cross-sectional regression aggregating 29 technical indicators generates a long-short portfolio with a mean weekly return of 3.82% (Newey-West t = 5.03) and an annualized Sharpe ratio of 4.54. The strategy delivers market-neutral alpha of 3.82% per week (t = 7.09) with a CAPM beta of 0.020. Three findings warrant emphasis. First, value-weighting destroys the alpha entirely, confirming concentration in smaller, dispersed names. Second, twelve of the top fifteen Elastic-Net coefficients are negative, indicating that the underlying pattern is short-term reversal rather than trend continuation. Third, returns are heavily regime-dependent, with Sharpe ratios near 1.0 in trending markets and above 7.0 in dispersive regimes. Cross-sectional dispersion-harvesting alpha persists in cryptocurrency markets, but its empirical realization is sharply sensitive to universe liquidity, weighting scheme, rebalance horizon, and market regime.

Ding Ding, Yang Li, Poh Ling Neo, Zhiyuan Wang · 5 authors

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets.

Sen Yang, Aviv Yaish, Arthur Gervais, Fan Zhang

Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.

Grigoriy Korolev

ABSTRACT We explore the risk‐return tradeoff of Decentralized Finance (DeFi). We construct three novel indices for different asset classes: Lending, Decentralized Exchanges, and Derivatives. Motivated by the cryptocurrency pricing framework of Liu and Tsyvinski, we investigate how DeFi assets comove with financial primitives. We document limited correlation with traditional equities, currencies, interest rates, and commodities. We further examine several DeFi‐specific factors. Bitcoin and Ethereum returns show no significant association with subsequent DeFi returns, highlighting a decoupling between base‐layer assets and application‐layer protocols. Meanwhile, we find some in‐sample associations with DeFi‐specific factors such as momentum, investor attention, and performance of centralized platforms. A novel book‐to‐market ratio constructed using Total Value Locked and market capitalization does not display a systematic relationship with returns. Finally, we find only limited and sector‐specific associations with traditional equity industries.

Mikhail Urinson

ThemeThe convergence of Artificial Intelligence (AI), Quantitative Finance, and Blockchain technologies is reshaping how capital is analyzed, deployed, and optimized across both traditional and decentralized markets.

Chen Ziwen

No abstract is available for this record.

Aktham Maghyereh, Basel Awartani

This study examines the latent common volatility factor in cryptocurrency markets using daily data for ten major cryptocurrencies from January 2018 to September 2025. It estimates the common volatility factor (COVOL) within the factor-volatility framework of Engle and Campos-Martins (2023) and it identifies its determinants using machine learning and SHAP analysis. Results reveal a statistically significant common volatility factor that intensifies during major macroeconomic events and crypto-specific shocks. Bitcoin exhibits the highest exposure, while global financial stress and investor sentiment are found to be the primary drivers. This paper provides the first direct estimation of a common volatility factor in cryptocurrency markets, demonstrating their increasing integration with global financial conditions and offering important implications for risk management and portfolio diversification.