Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Model ARCH (Autoregresivní podmíněná heteroskedasticita)× | Model GARCH (Predikce volatility)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1982 | 1986 |
| Tvůrce≠ | Robert F. Engle | Tim Bollerslev |
| Typ | Conditional volatility model | Conditional volatility model |
| Původní zdroj≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Další názvy | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Příbuzné≠ | 6 | 5 |
| Shrnutí≠ | The 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 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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