Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| DCC-GARCH (Dynamic Conditional Correlation)× | Modèle ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Domaine≠ | Finance | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2002 | 2015 |
| Auteur d'origine≠ | Robert F. Engle | Box & Jenkins (Box-Jenkins methodology) |
| Type≠ | Multivariate volatility model | Univariate time-series model |
| Source fondatrice≠ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Alias≠ | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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