Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Factores de Riesgo de Componentes Principales× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Finanzas | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1991 | 2019 |
| Autor original≠ | Litterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Statistical factor model (dimension reduction) | Linear regression |
| Fuente seminal≠ | Litterman, R. & Scheinkman, J. (1991). Common Factors Affecting Bond Returns. Journal of Fixed Income, 1(1), 54-61. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | risk factor PCA, return covariance decomposition, statistical factor model, Risk Faktörü PCA (Getiri Kovaryans Ayrışımı) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados | 5 | 5 |
| Resumen≠ | Risk Factor PCA is a dimension-reduction method that decomposes the return covariance matrix of many assets into a small set of orthogonal principal components interpreted as systematic risk factors. Litterman and Scheinkman (1991) used it to show that bond returns are driven by a few common factors, and Connor and Korajczyk (1988) developed the statistical-factor interpretation for the APT. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateConjunto de datos ↗ |
|
|