विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डीसीसी-गार्ग मॉडल (डायनामिक कंडीशनल कोरिलेशन)× | ARCH मॉडल (ऑटोरिग्रेसिव कंडीशनल हेटेरोस्केडैस्टिसिटी)× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2002 | 1982 |
| प्रवर्तक | Robert F. Engle | Robert F. Engle |
| प्रकार≠ | Multivariate volatility model | Conditional volatility 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 ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| उपनाम | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| 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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering. |
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