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Linganisha mbinu

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Mfumo Imara wa EGARCH×TGARCH Imara×
NyanjaEkonometrikiEkonometriki
FamiliaRegression modelRegression model
Mwaka wa asili20081994–2000s
MwanzilishiNelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authorsZakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature
AinaRobust volatility modelVolatility model with asymmetry and robust estimation
Chanzo asiliaMuler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. DOI ↗
Majina mbadalaRobust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCHrobust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH
Zinazohusiana66
MuhtasariRobust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect.Robust TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality.
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  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

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ScholarGateLinganisha mbinu: Robust EGARCH · Robust TGARCH. Imepatikana 2026-06-17 kutoka https://scholargate.app/sw/compare