Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| EGARCH (Exponential GARCH)× | Realizētā volatilitāte un HAR modelis× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Finanses |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1991 | 2009 |
| Autors≠ | Nelson | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Tips≠ | Conditional volatility model (asymmetric GARCH variant) | Time-series regression of realized variance |
| Pirmavots≠ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Citi nosaukumi≠ | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | EGARCH 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. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
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