Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Epälineaarinen GARCH-malli× | ARIMA-malli (Autoregressiivinen integroitu liukuva keskiarvo)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1991-1993 | 1970 |
| Kehittäjä≠ | Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH | George Box and Gwilym Jenkins |
| Tyyppi≠ | Volatility model | Time series forecasting model |
| Alkuperäislähde≠ | Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Rinnakkaisnimet | NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateAineisto ↗ |
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