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Muundo wa ARCH (Autoregressive Conditional Heteroskedasticity)×Modeli ya TGARCH (Threshold GARCH)×
NyanjaEkonometrikiEkonometriki
FamiliaRegression modelRegression model
Mwaka wa asili19821993-1994
MwanzilishiRobert F. EngleZakoian (1994); Glosten, Jagannathan & Runkle (1993)
AinaConditional volatility modelAsymmetric volatility model
Chanzo asiliaEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗
Majina mbadalaARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance modelThreshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Zinazohusiana66
MuhtasariThe 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 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.
ScholarGateSeti ya data
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  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

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