ScholarGate
Asisten

Bandingkan metode

Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.

GJR-GARCH (GARCH Asimetris)×Model GARCH (Peramalan Volatilitas)×
BidangEkonometrikaEkonometrika
KeluargaRegression modelRegression model
Tahun asal19931986
PencetusGlosten, Jagannathan & Runkle (1993); Zakoian (1994)Tim Bollerslev
TipeAsymmetric conditional volatility modelConditional volatility model
Sumber perintisGlosten, 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 ↗Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗
Aliasasymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Terkait55
RingkasanGJR-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).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.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 1 Sumber
  3. PUBLISHED

Ke halaman pencarian Unduh salindia

ScholarGateBandingkan metode: GJR-GARCH · GARCH Model. Diakses 2026-06-18 dari https://scholargate.app/id/compare