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| DCC-GARCH (Correlazione Condizionale Dinamica)× | Modello ARIMA (Autoregressive Integrated Moving Average)× | Modelli Copula (Gaussiana, t, Clayton, Gumbel, Frank)× | |
|---|---|---|---|
| Campo≠ | Finanza | Econometria | Finanza |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 2002 | 2015 | 1959 |
| Ideatore≠ | Robert F. Engle | Box & Jenkins (Box-Jenkins methodology) | Sklar (1959); dependence-concept treatment by Joe (1997) |
| Tipo≠ | Multivariate volatility model | Univariate time-series model | Dependence model |
| Fonte seminale≠ | 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 | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ |
| Alias≠ | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | 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). | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. |
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