This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.

The Bitcoin Lightning Network, a Layer-2 more scalable solution for the Bitcoin blockchain, has emerged to address the scalability challenges faced by the Bitcoin network. However, the centralization of the Lightning Network has been a growing concern, as the concentration of highly active nodes within it could compromise the decentralized nature of the Bitcoin ecosystem. In this research paper, we conduct a quantitative analysis of centralization in the Lightning Network using various centrality metrics, such as Gini coefficient, Nakamoto coefficient, Herfindahl-Hirschman Index, Theil Index and Shannon entropy. The proposed methodology includes data collection, clustering nodes into entities and setting up the experimental environment. The quantitative analysis of centralization in the BLN reveals complex results, with the Gini coefficient for node capacity distribution increasing from 0.85 to 0.97 over eight yearly timestamps, indicating growing inequality. Meanwhile, the Nakamoto coefficient fluctuated, suggesting that while control over network resources is uneven, it may still be more decentralized than previously thought.

As privacy and security concerns increase, Zero Knowledge Proof (ZKP) technology offers a promising solution for secure digital verification. ZKP addresses key privacy and security challenges across individual, business, and public sectors by enabling data protection without revealing sensitive information. The aim of this study is to analyse ZKP’s application areas by reviewing current literature and case studies, examining its strengths, limitations, and potential risks. Findings highlight the capability of ZKP to enhance privacy, security, and verification processes across various fields, including blockchain technology, identity authentication, secure data sharing, and digital voting systems. The paper provides a balanced perspective on ZKP’s benefits and challenges, including computational complexity and scalability issues. By suggesting practical use cases, this work aims to contribute to a deeper understanding of how ZKP technology can support innovation across various industries while addressing critical privacy and security needs.