Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| DCC-GARCH (Correlazione Condizionale Dinamica)× | Modello GARCH (Previsione della Volatilità)× | Modello di Autoregressione Vettoriale (VAR)× | |
|---|---|---|---|
| Campo≠ | Finanza | Econometria | Econometria |
| Famiglia | Regression model | Regression model | Regression model |
| Anno di origine≠ | 2002 | 1986 | 2005 |
| Ideatore≠ | Robert F. Engle | Tim Bollerslev | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tipo≠ | Multivariate volatility model | Conditional volatility model | Multivariate time-series 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 ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Alias | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Correlati≠ | 5 | 5 | 4 |
| 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. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
| ScholarGateInsieme di dati ↗ |
|
|
|