Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Robustti EGARCH-malli× | EGARCH-malli (Exponential GARCH)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2008 | 1991 |
| Kehittäjä≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Daniel B. Nelson |
| Tyyppi≠ | Robust volatility model | Volatility / conditional variance model |
| Alkuperäislähde≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Rinnakkaisnimet | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect. | 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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