เปรียบเทียบวิธี

	แบบจำลอง Nonlinear DCC-GARCH (Asymmetric Dynamic Conditional Correlation)	แบบจำลอง EGARCH (Exponential GARCH)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2006	1991
ผู้ริเริ่ม	Cappiello, Engle & Sheppard	Daniel B. Nelson
ประเภท	Multivariate volatility and correlation model	Volatility / conditional variance model
แหล่งต้นตำรับ	Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
ชื่อเรียกอื่น	ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
ที่เกี่ยวข้อง	2	6
สรุป	The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.