Cryptocurrency price bubble detection using log-periodic power law model and wavelet analysis

Zhang, Junhuan, Wang, Haodong, Chen, Jing ORCID: https://orcid.org/0000-0001-7135-2116 and Liu, Anqi ORCID: https://orcid.org/0000-0002-9224-084X 2024. Cryptocurrency price bubble detection using log-periodic power law model and wavelet analysis. IEEE Transactions on Engineering Management 71 , pp. 11796-1812. 10.1109/TEM.2024.3427647

PDF - Accepted Post-Print Version Download (8MB)

Abstract

In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Mathematics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9391
Date of First Compliant Deposit:	21 August 2024
Date of Acceptance:	3 July 2024
Last Modified:	23 Aug 2024 02:22
URI:	https://orca.cardiff.ac.uk/id/eprint/171510

Actions (repository staff only)

Edit Item