Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| DCC-GARCH (dinamiskā nosacītā korelācija)× | ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis× | EGARCH (Exponential GARCH)× | |
|---|---|---|---|
| Nozare≠ | Finanses | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model | Regression model |
| Izcelsmes gads≠ | 2002 | 2015 | 1991 |
| Autors≠ | Robert F. Engle | Box & Jenkins (Box-Jenkins methodology) | Nelson |
| Tips≠ | Multivariate volatility model | Univariate time-series model | Conditional volatility model (asymmetric GARCH variant) |
| Pirmavots≠ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ |
| Citi nosaukumi≠ | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH |
| Saistītās≠ | 5 | 5 | 4 |
| Kopsavilkums≠ | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. |
| ScholarGateDatu kopa ↗ |
|
|
|