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政大典藏 > College of Commerce > Department of Risk Management and Insurance > Periodical Articles > Item 140.119/156766

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/156766

Title:	Stablecoin depegging risk prediction
Authors:	謝明華 Hsieh, Ming-Hua;Chen, Lee, Yi-His;Chiu, Yu-Fen
Contributors:	風管系
Keywords:	Stablecoins;Depegging;Machine learning
Date:	2025-04
Issue Date:	2025-04-30 15:03:07 (UTC+8)
Abstract:	This study aims to identify and analyze key factors contributing to depegging risks in stablecoins, consolidating insights from the literature into four critical categories: trading price and volume, market information, sentiment, and volatility. Utilizing these insights, we develop predictive models using three machine learning algorithms—logistic regression, random forest, and XGBoost—to accurately and timely predict stablecoin depegging events. Our primary subjects are the top four stablecoins by daily trading volume: USDT, USDC, BUSD, and DAI. Diverging from previous studies that employed static depegging thresholds, we adopt a dynamic threshold adjusted for trading volume. Additionally, this study is the first to incorporate sentiment indicators from news sources alongside traditional on-chain price and volume data. Covering the empirical period from January 1, 2022, to December 31, 2023. Our findings confirm that significant fluctuations in mainstream cryptocurrencies (BTC and ETH) indeed influence stablecoin depegging. While past literature's instability measures provide early warning effects, the sentiment indicators surprisingly did not show significant early warning effects for our research subjects. The models developed enable crypto asset investors to predict the risk of stablecoin depegging promptly, facilitating informed investment decisions and reducing investment risks.
Relation:	Pacific-Basin Finance Journal, Vol.90, 102640
Data Type:	article
DOI link:	https://doi.org/10.1016/j.pacfin.2024.102640
DOI:	10.1016/j.pacfin.2024.102640
Appears in Collections:	[Department of Risk Management and Insurance] Periodical Articles

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