Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модели кредитного риска (Мертон, KMV, CreditMetrics)× | Логистическая регрессия× | |
|---|---|---|
| Область≠ | Финансы | Статистика исследований |
| Семейство≠ | Regression model | Process / pipeline |
| Год появления≠ | 1974 | 1958 |
| Автор метода≠ | Robert C. Merton (structural model); J.P. Morgan / Gupton et al. (CreditMetrics) | David Roxbee Cox |
| Тип≠ | Structural and portfolio credit risk model | Method |
| Основополагающий источник≠ | Merton, 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 ↗ |
| Другие названия≠ | Merton model, KMV model, CreditMetrics, structural credit risk model | logit model, binomial logistic regression, LR |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Credit 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. |
| ScholarGateНабор данных ↗ |
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