Blockchain Papers

Andreas Park

No abstract is available for this record.

Jan Kalina

Agent systems, particularly multi-agent systems, are becoming increasingly important tools for modeling and decision-making in complex environments, including finance, optimization, and epidemics. These systems simulate interactions between autonomous agents, which are individual entities that make decisions based on predefined rules, enabling the study of decentralized phenomena such as market behavior, social interactions, and information diffusion. By incorporating advanced statistical techniques, agent systems offer a more dynamic and adaptable approach compared to traditional, centralized models, capturing emergent behaviors and optimizing decisions in uncertain environments. This paper explores the role of agent systems in addressing challenges in complex domains, with a focus on their application in finance, optimization, epidemic modeling, and combating disinformation. The integration of agent-based models with principles of statistics and information theory is examined as a key factor driving the effectiveness of these systems in real-world applications. Through this examination, the paper highlights the growing significance of agent systems in tackling modern, decentralized problems that traditional methods have struggled to address.

Lai T. Hoang

This study shows that returns of cryptocurrencies with similar prices move together. This price-based comovement is independent of comovements caused by other cryptocurrencies’ well-known common risk factors including size, momentum, past returns, past trading volume, or market returns. The results are robust to alternative estimation methods and data frequencies. Additional analysis shows that the relationship becomes stronger during periods of high investor sentiment, exhibits a long-run reversal, and holds within a sample of memecoins. These findings support a sentiment-based explanation of return comovement.

Seyed Mohammad Habeli, Seyed Mahdi Barakchian, Ali Motavasseli

No abstract is available for this record.

Ran Duchin, Da Huang, Jeffrey Yang

No abstract is available for this record.

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

No abstract is available for this record.

Pablo Azar, Sergio Olivas, Nish D. Sinha

This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.

Sebastien Hitier

No abstract is available for this record.

Veronika Czellar, Engin Iyidogan

No abstract is available for this record.

Lydia Deborah Isaac, Srisanjana Arunkumar, Vigneshwaran Sundaramurthi, Gowthamraj Bommannan

No abstract is available for this record.

Zheng Cao

No abstract is available for this record.

Po‐Sheng Ko, Kuo‐Shing Chen

In the AI era, we contribute to the literature by uncovering that the price dynamics of most AI tokens could be fully characterized by the processes driven by fractal Brownian motion, which robustly supports the principles of the fractal markets hypothesis. Using rescaled range (R/S, i.e., Fractal) analysis and the wavelet coherence technique, we analyzed daily log-returns from seven major AI tokens and Bitcoin over the period 2020–2024. Our empirical results rejected the weak form of the Efficient Market Hypothesis (EMH), supporting the Fractal Market Hypothesis (FMH) as a better explanation for the dynamics of AI crypto tokens. More importantly, the log-returns of all analyzed AI tokens, each exhibiting a Hurst exponent exceeding 0.58, provided evidence of persistent behavior and an inherent tendency toward positive price trajectories. These results implied that Fractal analysis can enhance investors' ability to model return dynamics and identify potential appreciation in AI tokens, particularly as short-term trading activity intensifies during episodes of elevated market turbulence. Finally, this work reveals that AI tokens exhibit strong coherence patterns with Bitcoin, varying across time and frequency domains, suggesting Bitcoin's limited role as a hedge against AI tokens. Crucially, this study highlights the significant role of AI tokens as potential safe-haven assets during market turmoil, offering valuable insights for portfolio diversification for crypto investors with intuitive and plausible results that carry strong policy implications.

Jobaer Hossain

No abstract is available for this record.

Krishna Mula

This article examines the transformative evolution of transaction processing systems from traditional batch processing to real-time payment mechanisms. The historical progression and architectural distinctions between these paradigms while analyzing the critical transition factors that facilitated this evolution. The discussion encompasses the enabling technologies—including API-driven banking, distributed ledger solutions, and cloud computing infrastructure—that have revolutionized payment processing capabilities. Through the demonstration of current implementation cases across peer-to-peer transfers, business transactions, and international remittances, the article provides insights into practical applications and market adoption patterns. The exploration extends to emerging trends, including central bank digital currencies, artificial intelligence for fraud detection, and enhanced security frameworks. The article concludes with a forward-looking discussion of research imperatives addressing cross-border payment efficiency, monetary policy implications in real-time environments, and financial inclusion opportunities through modernized payment infrastructure. This comprehensive article provides valuable perspectives on the technological, operational, and policy dimensions of payment system evolution for financial professionals, technology implementers, and policy researchers.

M S Abhijith, S. Devika, M. Dhanya

No abstract is available for this record.

Cathy W. S. Chen, Po‐Hui Chen, Ying‐Lin Hsu

ABSTRACT Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. This research incorporates a threshold‐switching mechanism into Taylor's ES‐CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both Value‐at‐Risk (VaR) and Expected Shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus with the option to use the rolling standard deviation of returns as a short‐term volatility proxy as a regressor. We estimate the parameters and forecast tail risk within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, we assess the one‐step‐ahead forecasting performance over a four‐year out‐of‐sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.

Basma Almisshal, Halil İbrahim Bulut

No abstract is available for this record.

Zeinab Mohammad Ali, Asma Salman

No abstract is available for this record.

Shirui Wang, Tianyang Zhang

No abstract is available for this record.

Jacek Karasiński

Abstract The objective of this study was to examine the level and behaviour of the weak-form efficiency of the 16 most capitalised cryptocurrencies using intraday data. The study employed martingale difference hypothesis tests utilising the rolling window method. The predictability of high frequency returns varied over time. For most of the time, the cryptocurrencies were unpredictable. Nevertheless, their weak-form efficiency appeared to decrease along with an increase in frequency. In general, most cryptocurrencies were marked by high levels of unpredictability. However, there were some significant differences between the most and least efficient ones. To exploit market inefficiencies, investors should focus on higher frequencies. Higher frequencies should also be a concern to regulators when it comes to ensuring market efficiency.

Anthony Alexander

No abstract is available for this record.

Bahram Alidaee, Haibo Wang, Wendy Wang

Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds; they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.

<p>Zhongyuan Xu</p>

The cryptocurrency market poses a huge challenge to portfolio optimization due to its high volatility and complex market dynamics. To address these issues, this paper uses reinforcement learning (RL) algorithms for dynamic portfolio optimization, aiming to improve the return and risk control capabilities of the portfolio through intelligent decision-making. This paper adopts a strategy based on deep reinforcement learning. By interacting with the cryptocurrency market, the agent can continuously optimize asset allocation, maximize investment returns while controlling volatility. The experimental results show that compared with traditional strategies, the reinforcement learning model has obvious advantages in key indicators such as cumulative return rate, annualized volatility, maximum drawdown and Sharpe ratio. Specifically, the cumulative return rate of the reinforcement learning model reaches 85.12%, the annualized volatility is 45.76%, and the maximum drawdown is controlled at -22.34%, showing strong income acquisition and risk management capabilities. In addition, the dynamic adjustment of asset allocation has optimized the weights of various cryptocurrencies, effectively dispersed risks, and improved the overall performance of the investment portfolio.

Rubaiyat Ahsan Bhuiyan, Tanusree Chakravarty Mukherjee, Kazi Md. Tarique, Ch. Zhang

In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the return–volatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession Ⅲ (a Russia-Ukraine war); next, part Ⅳ — the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.