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| ARIMA (Autoregressive Integrated Moving Average) 모형× | 극단값 이론 (Extreme Value Theory, EVT)× | |
|---|---|---|
| 분야≠ | 계량경제학 | 재무학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2015 | 2001 |
| 창시자≠ | Box & Jenkins (Box-Jenkins methodology) | Coles (textbook treatment); McNeil, Frey & Embrechts |
| 유형≠ | Univariate time-series model | Tail / extreme-event 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 | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 |
| 별칭≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold |
| 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). | Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold. |
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