विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डीसीसी-गार्ग मॉडल (डायनामिक कंडीशनल कोरिलेशन)× | EGARCH मॉडल (घातीय GARCH)× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2002 | 1991 |
| प्रवर्तक≠ | Robert F. Engle | Daniel B. Nelson |
| प्रकार≠ | Multivariate volatility model | Volatility / conditional variance model |
| मौलिक स्रोत≠ | 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 ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| उपनाम | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| संबंधित≠ | 5 | 6 |
| सारांश≠ | 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. | 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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