Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| GARCH-modellen (prognostisering av volatilitet)× | Vektorautoregressionsmodell (VAR)× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1986 | 2005 |
| Upphovsperson≠ | Tim Bollerslev | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Typ≠ | Conditional volatility model | Multivariate time-series model |
| Ursprungskälla≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Alias | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
| ScholarGateDatamängd ↗ |
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