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Model Purata Bergerak Tak Linear (NMA)×Model GARCH (Peramalan Volatiliti)×
BidangEkonometrikEkonometrik
KeluargaRegression modelRegression model
Tahun asal19781986
PengasasGranger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)Tim Bollerslev
JenisNonlinear time series modelConditional volatility model
Sumber perintisGranger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen. link ↗Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗
AliasNMA model, nonlinear moving average, NLMA model, nonlinear MAGARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Berkaitan45
RingkasanThe Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion.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.
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ScholarGateBandingkan kaedah: Nonlinear MA model · GARCH Model. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare