ScholarGate
Trợ lý

So sánh phương pháp

Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.

Các mô hình rủi ro tín dụng (Merton, KMV, CreditMetrics)×Hồi quy Logistic×
Lĩnh vựcTài chínhThống kê nghiên cứu
HọRegression modelProcess / pipeline
Năm ra đời19741958
Người khởi xướngRobert C. Merton (structural model); J.P. Morgan / Gupton et al. (CreditMetrics)David Roxbee Cox
LoạiStructural and portfolio credit risk modelMethod
Công trình gốcMerton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 29(2), 449-470. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Tên gọi khácMerton model, KMV model, CreditMetrics, structural credit risk modellogit model, binomial logistic regression, LR
Liên quan53
Tóm tắtCredit risk models estimate the probability that a borrower defaults and the resulting distribution of credit losses. The structural approach was introduced by Robert C. Merton in 1974, treating a firm's equity as a call option on its assets, and was later extended into the KMV distance-to-default framework and the CreditMetrics rating-transition portfolio model published by J.P. Morgan in 1997.Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.
ScholarGateBộ dữ liệu
  1. v1
  2. 2 Nguồn tài liệu
  3. PUBLISHED
  1. v1
  2. 2 Nguồn tài liệu
  3. PUBLISHED

Đến trang tìm kiếm Tải xuống bản trình chiếu

ScholarGateSo sánh phương pháp: Credit Risk Models · Logistic Regression. Truy cập ngày 2026-06-19 từ https://scholargate.app/vi/compare