השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מודל Panel DCC-GARCH× | Panel TGARCH (Threshold GARCH for Panel Data)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 2002 | 1993–1994 (panel extension: 2000s onward) |
| הוגה השיטה≠ | Robert F. Engle | Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature |
| סוג≠ | Multivariate volatility model | Asymmetric conditional volatility model |
| מקור מכונן≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ | Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. DOI ↗ |
| כינויים | DCC-GARCH panel, panel dynamic conditional correlation, multivariate DCC-GARCH, Panel DCC | Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model |
| קשורות≠ | 5 | 4 |
| תקציר≠ | The Panel DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation GARCH framework to panel data settings, jointly modelling time-varying volatility and cross-sectional correlations across multiple units (countries, firms, or assets) over time. It allows pairwise correlations to vary dynamically in response to market shocks while preserving parsimony via a two-step estimation. | Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more efficient parameter estimates. |
| ScholarGateמערך נתונים ↗ |
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