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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141060


    Title: 最適預期損失信用評等模型:以P2P平台為例
    Optimal Structure of Expected Loss Credit Rating Model:Evidence from P2P Platform
    Authors: 簡銘均
    Jian, Ming-Chun
    Contributors: 呂桔誠
    林士貴
    Lyu, Joseph Jye-Cherng
    Lin, Shih-Kuei
    簡銘均
    Jian, Ming-Chun
    Keywords: 違約風險
    提前還款風險
    違約損失率
    預期損失
    機器學習
    P2P平台風險管理
    Default Risk
    Prepayment Risk
    Loss Given Default
    Expected Loss
    Machine Learning
    Risk Management in P2P Platform
    Date: 2022
    Issue Date: 2022-08-01 17:28:49 (UTC+8)
    Abstract: 本研究使用 P2P 平台資料提出一個最適結構之預期損失信用評等模型，不僅包含違約風險，也考量提前還款風險、違約損失率以及違約曝險額。建構預期損失模型過程中，我們透過機器學習方法建構各風險成分模型並使用 SHAP 值確保機器學習的解釋能力與傳統回歸模型有一致的結果。此外，我們使用 Kruskal-Wallis 檢定以及 Conover 檢定發現以預期損失信用評等模型相較違約機率信用評等模型，均有較好的分辨能力。最後，我們發現將提前還款風險納入評等模型中可以使模型對於預測借款者最終損失的表現有所提升。
    In this study, we propose an optimal structured expected loss credit rating model using P2P platform data, including default risk, prepayment risk, loss given default (LGD), and exposure at default (EAD). In addition, each risk component of expected loss is constructed via a machine learning approach, and we use SHAP values to ensure the explanatory power of the machine learning model is consistent with the statistical regression model. Furthermore, using the Kruskal-Wallis and the Conover’s tests, we find that the expected loss credit rating model has better discriminatory power than the probability of default credit rating model. Finally, incorporating prepayment risk into the credit rating model improves the model’s performance in predicting the borrower’s actual loss.
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    Description: 碩士
    國立政治大學
    金融學系
    109352011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109352011
    Data Type: thesis
    DOI: 10.6814/NCCU202200929
    Appears in Collections:[Department of Money and Banking] Theses

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