Nonlinear connectedness of conventional crypto-assets and sustainable crypto-assets with climate change: A complex systems modelling approach. [PDF]
PLoS OneKhan MH, Macherla S, Anupam A.
europepmc +1 more source
Disagreement and returns: The case of cryptocurrencies
Financial Management, Volume 54, Issue 3, Page 633-672, Autumn (Fall) 2025.Abstract We present the first evidence of investor‐trading‐based disagreement's influence on cross‐sectional cryptocurrency daily returns. We interpret abnormal trading volume as investor disagreement and find evidence in support of Miller's disagreement model: when short‐sale constraints are binding, high abnormal volume (high disagreement) assets ...
Jon A. Garfinkel +2 more
wiley +1 more source
The reversal in the cryptocurrency market before and during the Covid-19 pandemic: Does investor attention matter? [PDF]
PLoS OnePham H, Tran TND, Nguyen NTT, Duong KD.
europepmc +1 more source
A Dynamical Systems Approach to Cryptocurrency Stability
, 2018 Carey Caginalp
openalex +2 more sources
Weighted Moving Average of Forecasting Method for Predicting Bitcoin Share Price using High Frequency Data: A Statistical Method in Financial Cryptocurrency Technology [PDF]
, 2018 Nashirah Abu Bakar, Sofian Rosbi
openalex +1 more source
Non‐Fungible Tokens and Consumer Behavior: A Systematic Literature Review and Research Agenda
International Journal of Consumer Studies, Volume 49, Issue 5, September 2025.ABSTRACT Non‐fungible tokens (NFTs) are digital artifacts built on blockchain technology that have achieved notoriety for their rapid consumer adoption, technical sophistication, and dramatic price swings. This paper synthesizes contemporary academic research on NFT consumer behavior to better understand the current state of the field, to explore its ...
Paul Griffiths +2 more
wiley +1 more source
Computing Minimum Weight Cycles to Leverage Mispricings in Cryptocurrency Market Networks
, 2018 Francesco Bortolussi +2 more
openalex +2 more sources
Forecasting value‐at‐risk for cryptocurrencies
International Review of Finance, Volume 25, Issue 3, September 2025.Abstract Value‐at‐Risk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the time‐heterogeneous Student's t autoregressive model outperforms standard models commonly used by ...
Michael Michaelides, Niraj Poudyal
wiley +1 more source