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सामान्यीकृत ऑटोरेग्रेसिव कंडीशनल हेटेरोस्केडैस्टिसिटी (GARCH)×साधारण न्यूनतम वर्ग (OLS) समाश्रयण×SARIMA (Seasonal ARIMA)×
क्षेत्रअर्थमितिअर्थमितिअर्थमिति
परिवारRegression modelRegression modelRegression model
उद्भव वर्ष198620192015
प्रवर्तकTim BollerslevWooldridge (textbook treatment); classical least squaresBox & Jenkins (seasonal extension of ARIMA)
प्रकारConditional volatility modelLinear regressionSeasonal time-series model
मौलिक स्रोतBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Box, 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
उपनामGARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeliordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuseasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
संबंधित555
सारांशGARCH 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.Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.
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