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    題名: Stablecoin depegging risk prediction
    作者: 謝明華
    Hsieh, Ming-Hua;Chen, Lee, Yi-His;Chiu, Yu-Fen
    貢獻者: 風管系
    關鍵詞: Stablecoins;Depegging;Machine learning
    日期: 2025-04
    上傳時間: 2025-04-30 15:03:07 (UTC+8)
    摘要: 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.
    關聯: Pacific-Basin Finance Journal, Vol.90, 102640
    資料類型: article
    DOI 連結: https://doi.org/10.1016/j.pacfin.2024.102640
    DOI: 10.1016/j.pacfin.2024.102640
    顯示於類別:[風險管理與保險學系] 期刊論文

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