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비선형 TGARCH 모형×ARCH 모형 (자기회귀 조건부 이분산성)×EGARCH 모형 (Exponential GARCH)×
분야계량경제학계량경제학계량경제학
계열Regression modelRegression modelRegression model
기원 연도1993–199419821991
창시자Jean-Michel Zakoian; related work by Glosten, Jagannathan & RunkleRobert F. EngleDaniel B. Nelson
유형Conditional heteroskedasticity modelConditional volatility modelVolatility / conditional variance model
원전Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗
별칭NL-TGARCH, Nonlinear Threshold GARCH, Asymmetric TGARCH, GJR-GARCH variantARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance modelExponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
관련466
요약The Nonlinear TGARCH (Threshold GARCH) model extends the standard GARCH framework by allowing positive and negative shocks of equal magnitude to exert different effects on future volatility. It models conditional volatility in terms of the absolute value of lagged residuals split by a sign threshold, capturing the well-documented leverage effect in financial return series.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 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.
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ScholarGate방법 비교: Nonlinear TGARCH model · ARCH model · EGARCH model. 2026-06-19에 다음에서 검색함: https://scholargate.app/ko/compare