Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Bayesian TGARCH (Threshold GARCH yenye Makadirio ya Bayesian)× | Modeli ya EGARCH (Exponential GARCH)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1994 / 2008 | 1991 |
| Mwanzilishi≠ | Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008) | Daniel B. Nelson |
| Aina≠ | Volatility model with asymmetric threshold and Bayesian inference | Volatility / conditional variance model |
| Chanzo asilia≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Majina mbadala | Bayesian TGARCH, Bayesian GJR-GARCH, Threshold GARCH with Bayesian estimation, TGARCH-B | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
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