เปรียบเทียบวิธี

	GJR-GARCH (GARCH แบบไม่สมมาตร)	แบบจำลอง ARCH (Autoregressive Conditional Heteroskedasticity)	Exponential GARCH (EGARCH)	แบบจำลอง GARCH (การพยากรณ์ความผันผวน)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model	Regression model	Regression model
ปีกำเนิด	1993	1982	1991	1986
ผู้ริเริ่ม	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)	Robert F. Engle	Nelson	Tim Bollerslev
ประเภท	Asymmetric conditional volatility model	Conditional volatility model	Conditional volatility model (asymmetric GARCH variant)	Conditional volatility model
แหล่งต้นตำรับ	Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
ชื่อเรียกอื่น	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
ที่เกี่ยวข้อง	5	6	4	5
สรุป	GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	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.