Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | Модели копул (Гауссовы, t, Клейтона, Гумбеля, Франка)× | Теория экстремальных значений (Extreme Value Theory, EVT)× | |
|---|---|---|---|
| Область≠ | Эконометрика | Финансы | Финансы |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 2015 | 1959 | 2001 |
| Автор метода≠ | Box & Jenkins (Box-Jenkins methodology) | Sklar (1959); dependence-concept treatment by Joe (1997) | Coles (textbook treatment); McNeil, Frey & Embrechts |
| Тип≠ | Univariate time-series model | Dependence model | Tail / extreme-event model |
| Основополагающий источник≠ | 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 | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 |
| Другие названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold |
| Связанные | 5 | 5 | 5 |
| Сводка≠ | 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). | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. | 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. |
| ScholarGateНабор данных ↗ |
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