Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Epälineaarinen liukuvan keskiarvon (NMA) malli× | GARCH-malli (volatiliteetin ennustaminen)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1978 | 1986 |
| Kehittäjä≠ | Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory) | Tim Bollerslev |
| Tyyppi≠ | Nonlinear time series model | Conditional volatility model |
| Alkuperäislähde≠ | Granger, 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 ↗ |
| Rinnakkaisnimet | NMA model, nonlinear moving average, NLMA model, nonlinear MA | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | The 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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