পদ্ধতির তুলনা করুন

	GARCH	ARIMA (Autoregressive Integrated Moving Average) মডেল	DCC-GARCH (Dynamic Conditional Correlation)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থায়ন
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1986	2015	2002
প্রবর্তক	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Robert F. Engle
ধরন	Conditional volatility model	Univariate time-series model	Multivariate volatility model
মৌলিক উৎস	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.
অপর নাম	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon
সম্পর্কিত	5	5	5
সারসংক্ষেপ	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.