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Fourier EGARCH: 부드러운 구조적 변화를 포함하는 변동성 모델링×GJR-GARCH (비대칭 GARCH)×
분야계량경제학계량경제학
계열Regression modelRegression model
기원 연도2010s1993
창시자Extension of Nelson (1991) EGARCH using Fourier approximation frameworksGlosten, Jagannathan & Runkle (1993); Zakoian (1994)
유형Volatility model with smooth structural breaksAsymmetric conditional volatility model
원전Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599. DOI ↗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. DOI ↗
별칭Fourier-EGARCH, F-EGARCH, Fourier exponential GARCH, smooth structural break EGARCHasymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)
관련35
요약Fourier EGARCH extends Nelson's (1991) Exponential GARCH model by embedding Fourier trigonometric terms in the conditional variance equation to capture smooth, gradual shifts in the unconditional variance level over time. This allows the model to handle structural breaks in volatility without requiring prior knowledge of their timing or number.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).
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