This study undertakes a comprehensive examination of Non-Fungible Tokens (NFTs) within the Metaverse, starting with a review of the Metaverse's evolution, the emergence of NFTs, and the transformative benefits of the Metaverse. It explains the concepts of Web2 and Web3, providing a comparative analysis between historical and contemporary iterations of the Metaverse. This foundational review sets the stage for an in-depth empirical analysis of five notable NFTs: AXS, MANA, ENJ, THETA, and SAND. Utilizing data sourced from CoinMarketCap spanning November 2020 to October 2022, the study employs a multifaceted analytical approach encompassing descriptive statistics, Hill's estimator, detrended fluctuation analysis, volatility clustering, asymmetric volatility clustering, and quantile-on-quantile regression analysis. This rigorous methodology uncovers a set of stylized facts that characterize the unique market behaviors, pricing mechanisms, and investor dynamics within the Metaverse NFT space. The findings not only illuminate the nuanced complexities of the NFT market but also provide critical insights for investors, content creators, and policymakers, highlighting the necessity for innovative strategies and regulatory considerations in navigating the evolving Metaverse ecosystem. To bolster the robustness of our findings, a comparative analysis was conducted using Metaverse indices from Bloomberg (BB) and data provided by Yield Guild Games (YGG). This study not only enriches the academic discourse on digital assets but also lays a groundwork for future research and development in the domain of Metaverse NFTs.  

Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.

This study undertakes a comprehensive examination of Non-Fungible Tokens (NFTs) within the Metaverse, starting with a review of the Metaverse's evolution, the emergence of NFTs, and the transformative benefits of the Metaverse. It explains the concepts of Web2 and Web3, providing a comparative analysis between historical and contemporary iterations of the Metaverse. This foundational review sets the stage for an in-depth empirical analysis of five notable NFTs: AXS, MANA, ENJ, THETA, and SAND. Utilizing data sourced from CoinMarketCap spanning November 2020 to October 2022, the study employs a multifaceted analytical approach encompassing descriptive statistics, Hill's estimator, detrended fluctuation analysis, volatility clustering, asymmetric volatility clustering, and quantile-on-quantile regression analysis. This rigorous methodology uncovers a set of stylized facts that characterize the unique market behaviors, pricing mechanisms, and investor dynamics within the Metaverse NFT space. The findings not only illuminate the nuanced complexities of the NFT market but also provide critical insights for investors, content creators, and policymakers, highlighting the necessity for innovative strategies and regulatory considerations in navigating the evolving Metaverse ecosystem.

To bolster the robustness of our findings, a comparative analysis was conducted using Metaverse indices from Bloomberg (BB) and data provided by Yield Guild Games (YGG).   

This study not only enriches the academic discourse on digital assets but also lays a groundwork for future research and development in the domain of Metaverse NFTs.

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.

We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.