Uncertainty and Bubbles in Cryptocurrencies: Evidence from Newly Developed Uncertainty Indices

Chowdhury, Md Shahedur R.; Damianov, Damian S.

Abstract

In this paper, we examine whether newly developed crypto price and policy uncertainty indices based on news coverage (Lucey et al., 2022) are associated with the emergence of bubbles in cryptocurrencies. Using probit regressions, we show that these indices have a higher explanatory power than factors previously considered in the literature. Furthermore, using a random forest model, we show that these classifiers are associated with the largest information gain (reduction in the Gini impurity measure) of the model. While the COVID-19 pandemic has exacerbated the occurrence of bubbles, these crypto uncertainty indices remain the best predictors of bubbles both before and during the pandemic. These results are robust to alternative definitions of a bubble, variations in the time horizon, and the inclusion of various regressors known to be related to the price movements in crypto assets.

Citation

Chowdhury, M. S. R., & Damianov, D. S. (2023). Uncertainty and Bubbles in Cryptocurrencies: Evidence from Newly Developed Uncertainty Indices. International Review of Financial Analysis, Article 102949. https://doi.org/10.1016/j.irfa.2023.102949

• Journal Article Type: Article
• Acceptance Date: Sep 14, 2023
• Online Publication Date: Sep 20, 2023
• Publication Date: 2023
• Deposit Date: Sep 27, 2023
• Publicly Available Date: Mar 21, 2025
• Journal: International Review of Financial Analysis
• Print ISSN: 1057-5219
• Electronic ISSN: 1873-8079
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 102949
• DOI: https://doi.org/10.1016/j.irfa.2023.102949
• Public URL: https://durham-repository.worktribe.com/output/1748038