Vertaile menetelmiä
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| ARIMA (Autoregressive Integrated Moving Average) -malli× | DCC-GARCH (Dynamic Conditional Correlation)× | |
|---|---|---|
| Tieteenala≠ | Ekonometria | Rahoitus |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2015 | 2002 |
| Kehittäjä≠ | Box & Jenkins (Box-Jenkins methodology) | Robert F. Engle |
| Tyyppi≠ | Univariate time-series model | Multivariate volatility model |
| Alkuperäislähde≠ | 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 | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ |
| Rinnakkaisnimet≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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). | 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. |
| ScholarGateAineisto ↗ |
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