방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 지수적 GARCH (EGARCH)× | ARIMA (Autoregressive Integrated Moving Average) 모형× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1991 | 2015 |
| 창시자≠ | Nelson | Box & Jenkins (Box-Jenkins methodology) |
| 유형≠ | Conditional volatility model (asymmetric GARCH variant) | Univariate time-series model |
| 원전≠ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. 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 |
| 별칭≠ | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | 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). |
| ScholarGate데이터셋 ↗ |
|
|