Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| APARCH× | GJR-GARCH (Asimetriskais GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads | 1993 | 1993 |
| Autors≠ | Ding, Granger & Engle | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) |
| Tips≠ | Conditional heteroscedasticity model | Asymmetric conditional volatility model |
| Pirmavots≠ | 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 ↗ | Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. DOI ↗ |
| Citi nosaukumi | Asymmetric Power ARCH, Power ARCH, APGARCH, Asimetrik Güç ARCH | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) |
| Saistītās≠ | 3 | 5 |
| Kopsavilkums≠ | 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. | GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994). |
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