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| ARIMA (Autoregressive Integrated Moving Average) 모형× | DCC-GARCH (동적 조건부 상관관계)× | |
|---|---|---|
| 분야≠ | 계량경제학 | 재무학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015 | 2002 |
| 창시자≠ | Box & Jenkins (Box-Jenkins methodology) | Robert F. Engle |
| 유형≠ | Univariate time-series model | Multivariate volatility model |
| 원전≠ | 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 ↗ |
| 별칭≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
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