مقایسهٔ روشها
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| مدل شرطی پویا بیزی (Bayesian DCC-GARCH)× | مدل بیزی TGARCH (مدل GARCH آستانهای با برآورد بیزی)× | |
|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی |
| خانواده | Regression model | Regression model |
| سال پیدایش≠ | 2002 (DCC); 2000s (Bayesian extension) | 1994 / 2008 |
| پدیدآور≠ | Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards) | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) |
| نوع≠ | Multivariate volatility model | Volatility model with asymmetric threshold and Bayesian inference |
| منبع بنیادین≠ | 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 ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| نامهای دیگر | Bayesian DCC-GARCH, Bayesian Dynamic Conditional Correlation, MCMC DCC-GARCH, Bayesian multivariate volatility model | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B |
| مرتبط | 6 | 6 |
| خلاصه≠ | Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH. | 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. |
| ScholarGateمجموعهداده ↗ |
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