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| Robust Dynamic Conditional Correlation GARCH (Robust DCC-GARCH)× | Robust EGARCH Model× | |
|---|---|---|
| Fagområde | Økonometri | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2002–2021 | 2008 |
| Ophavsperson≠ | Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021) | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors |
| Type≠ | Multivariate volatility model with robust estimation | Robust volatility model |
| Oprindelig kilde≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350. DOI ↗ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ |
| Aliasser | robust DCC-GARCH, robust dynamic conditional correlation, outlier-robust DCC, composite-likelihood DCC-GARCH | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH |
| Relaterede | 6 | 6 |
| Resumé≠ | The Robust DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by replacing standard quasi-maximum likelihood estimation with outlier-resistant or composite-likelihood techniques. This preserves accurate time-varying correlation estimation even when financial return data contain extreme observations, heavy tails, or structural irregularities. | 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. |
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