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ARCH 모형 (자기회귀 조건부 이분산성)×GARCH 모형 (변동성 예측)×TGARCH 모형 (Threshold GARCH)×
분야계량경제학계량경제학계량경제학
계열Regression modelRegression modelRegression model
기원 연도198219861993-1994
창시자Robert F. EngleTim BollerslevZakoian (1994); Glosten, Jagannathan & Runkle (1993)
유형Conditional volatility modelConditional volatility modelAsymmetric volatility model
원전Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗
별칭ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance modelGARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
관련656
요약The 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 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.The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.
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ScholarGate방법 비교: ARCH model · GARCH Model · TGARCH model. 2026-06-19에 다음에서 검색함: https://scholargate.app/ko/compare