विधियों की तुलना करें

	ARCH मॉडल (ऑटोरिग्रेसिव कंडीशनल हेटेरोस्केडैस्टिसिटी)	डीसीसी-गार्ग मॉडल (डायनामिक कंडीशनल कोरिलेशन)	गार्छ मॉडल (अस्थिरता पूर्वानुमान)
क्षेत्र	अर्थमिति	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model	Regression model
उद्भव वर्ष	1982	2002	1986
प्रवर्तक	Robert F. Engle	Robert F. Engle	Tim Bollerslev
प्रकार	Conditional volatility model	Multivariate volatility model	Conditional volatility model
मौलिक स्रोत	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	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.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
उपनाम	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
संबंधित	6	5	5
सारांश	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.	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 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.