Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська модель ARCH× | Модель DCC-GARCH (динамічна умовна кореляція)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1982 (ARCH); 1989 (Bayesian estimation) | 2002 |
| Автор методу≠ | Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989) | Robert F. Engle |
| Тип≠ | Volatility model with Bayesian inference | Multivariate volatility model |
| Основоположне джерело≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. 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 | Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | The Bayesian ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH. | 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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