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Exponential GARCH (EGARCH)×Autoregressiu Condicional Heteroscedàstic Generalitzat (GARCH)×Regressió per Mínims Quadrats Ordinàris (MQO)×
CampEconometriaEconometriaEconometria
FamíliaRegression modelRegression modelRegression model
Any d'origen199119862019
Autor originalNelsonTim BollerslevWooldridge (textbook treatment); classical least squares
TipusConditional volatility model (asymmetric GARCH variant)Conditional volatility modelLinear regression
Font seminalNelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Àliesexponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCHGARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeliordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionats455
ResumEGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.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).
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ScholarGateCompara mètodes: EGARCH · GARCH · OLS Regression. Recuperat el 2026-06-19 de https://scholargate.app/ca/compare