Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika mainīgo parametru ARCH modelis (TVP-ARCH)× | EGARCH modelis (eksponenciālais GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1980s–1990s | 1991 |
| Autors≠ | Extension of Engle (1982) ARCH; TVP-ARCH formalization credited to Nicholls & Quinn and subsequent state-space literature | Daniel B. Nelson |
| Tips≠ | Conditional heteroscedasticity model with time-varying coefficients | Volatility / conditional variance model |
| Pirmavots≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Citi nosaukumi | TVP-ARCH, time-varying ARCH, adaptive ARCH, state-space ARCH | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | The Time-Varying Parameter ARCH (TVP-ARCH) model extends the classic ARCH framework by allowing both the conditional mean coefficients and the ARCH variance parameters to drift over time according to a random-walk or state-space process. This makes it possible to capture structural shifts in volatility dynamics without imposing a fixed parameter regime. | The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
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