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Autoregressiivisen ehdollisen heteroskedastisuuden (ARCH) malli×DCC-GARCH-malli (dynaaminen ehdollinen korrelaatio)×
TieteenalaEkonometriaEkonometria
MenetelmäperheRegression modelRegression model
Syntyvuosi19822002
KehittäjäRobert F. EngleRobert F. Engle
TyyppiConditional volatility modelMultivariate volatility model
AlkuperäislähdeEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗
RinnakkaisnimetARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance modelDCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Liittyvät65
TiivistelmäThe 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 DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.
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ScholarGateVertaile menetelmiä: ARCH model · DCC-GARCH model. Haettu 2026-06-17 osoitteesta https://scholargate.app/fi/compare