Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Contrastación de VaR (Value-at-Risk)× | Regresión por Mínimos Cuadrados Ordinarios (MCO)× | |
|---|---|---|
| Campo≠ | Finanzas | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1998 | 2019 |
| Autor original≠ | Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Statistical hypothesis tests on VaR violation sequences | Linear regression |
| Fuente seminal≠ | Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | VaR backtest, Kupiec test, Christoffersen test, Dynamic Quantile test | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | VaR backtesting is a family of statistical tests that validate a risk model by comparing its Value-at-Risk forecasts against realised losses. It builds on Kupiec's (1995) unconditional coverage test, Christoffersen's (1998) conditional coverage test, and the Engle-Manganelli Dynamic Quantile (DQ) test. | 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 ↗ |
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