Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| APARCH× | Model GARCH (Prognoza volatilității)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1993 | 1986 |
| Autorul original≠ | Ding, Granger & Engle | Tim Bollerslev |
| Tip≠ | Conditional heteroscedasticity model | Conditional volatility model |
| Sursa seminală≠ | Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Denumiri alternative | Asymmetric Power ARCH, Power ARCH, APGARCH, Asimetrik Güç ARCH | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | APARCH, introduced by Ding, Granger, and Engle (1993) while studying long-memory properties of stock market returns, extends the GARCH family by allowing both the power transformation of conditional volatility and an asymmetric response to positive and negative shocks. The model nests at least seven well-known ARCH-type specifications as special cases, making it a unifying framework for volatility modelling in financial econometrics. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
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