Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Нелінійна модель DCC-GARCH (асиметрична динамічна умовна кореляція)× | Модель DCC-GARCH (динамічна умовна кореляція)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2006 | 2002 |
| Автор методу≠ | Cappiello, Engle & Sheppard | Robert F. Engle |
| Тип≠ | Multivariate volatility and correlation model | Multivariate volatility model |
| Основоположне джерело≠ | Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572. DOI ↗ | 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 ↗ |
| Інші назви | ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Пов'язані≠ | 2 | 5 |
| Підсумок≠ | The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news. | The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series. |
| ScholarGateНабір даних ↗ |
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