Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| DCC-GARCH (Динамична условна корелация)× | Модел GARCH (Прогнозиране на волатилността)× | |
|---|---|---|
| Област≠ | Финанси | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2002 | 1986 |
| Създател≠ | Robert F. Engle | Tim Bollerslev |
| Тип≠ | Multivariate volatility model | Conditional volatility model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Свързани | 5 | 5 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
|
|