Stephen Chan

We study the distributional and tail-risk properties of Bitcoin and the major cryptocurrencies using daily returns from June 2014 to May 2026. For a panel of 10 assets we fit a family of heavy-tailed distributions by maximum likelihood, rank them by the Akaike and Bayesian information criteria, and estimate the risk quantities that matter for management: Value-at-Risk, Expected Shortfall, and extreme-value tail indices. Returns are decisively non-Gaussian. Normality is rejected for every asset, excess kurtosis ranges from single digits to several dozen, and on the Bayesian information criterion the parsimonious Student- t and skewed generalized- t are preferred for almost every asset (4 and 5 of 10, respectively), while the heavily parameterised generalised hyperbolic, despite the highest raw likelihood, is selected for at most one; the lighter Akaike penalty moves more assets to the generalised hyperbolic (4 of 10) but never to the Gaussian. The Gaussian model understates the 99 % daily Value-at-Risk of Bitcoin by roughly a fifth relative to both the empirical and the extreme-value estimates, and Expected Shortfall is materially larger still. A provider-controlled refit on the 2014-2016 window changes the fitted degrees of freedom by a median of only 9 %, confirming that these results are not an artefact of the data source. We translate the statistics into concrete guidance for risk measurement, product design, and investor communication, and we set out a companion agenda on volatility dynamics and cross-asset dependence.

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.

Decentralized finance, or ‘De-Fi’, is an emerging sector and movement in finance and the cryptocurrency space that aims to extend the idea of digital currencies to a global decentralized financial system. In many cases, customized ‘coins’ or ‘tokens’ are used for applications such as borrowing or lending, providing liquidity, and even voting. Built on the same foundations of traditional cryptocurrencies (e.g. Bitcoin), these tokens possess monetary value and can be traded using fiat currencies on specialized decentralized exchanges. We provide the first analysis investigating the market efficiency of the decentralized finance market through DeFi tokens. Our findings from applying the adaptive market hypothesis (AMH) revealed that the efficiency of the markets varies over time, with the majority of the DeFi token returns exhibit very short days of inefficiency and predictability in their price every year. This is consistent with the AMH, but perhaps unexpected when considering the link between emerging financial markets and market efficiency. We conclude that the majority of investors and practitioners purchase these DeFi tokens for their utility value rather than for investment purposes, hence making the DeFi market more efficient. Further robustness checks on other comparable products in the blockchain ecosystem such as NFTs also reveal similar results.