Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский TGARCH (Threshold GARCH с Байесовской оценкой)× | Модель DCC-GARCH (динамическая условная корреляция)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1994 / 2008 | 2002 |
| Автор метода≠ | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) | Robert F. Engle |
| Тип≠ | Volatility model with asymmetric threshold and Bayesian inference | Multivariate volatility model |
| Основополагающий источник≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. 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 ↗ |
| Другие названия | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Bayesian TGARCH combines the Threshold GARCH volatility model — which captures the asymmetric response of volatility to positive versus negative shocks — with full Bayesian inference via Markov Chain Monte Carlo sampling. The result is a principled, uncertainty-aware framework for modeling leverage effects and fat-tailed financial returns. | 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. |
| ScholarGateНабор данных ↗ |
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