Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Value at Risk (VaR)× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2007 | 2015 |
| Автор метода≠ | Jorion (textbook benchmark); popularised by RiskMetrics / J.P. Morgan | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Financial risk measure | Univariate time-series model |
| Основополагающий источник≠ | Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill. ISBN: 978-0071464956 | 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 |
| Другие названия≠ | VaR, value-at-risk, delta-normal VaR, historical simulation VaR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Связанные | 5 | 5 |
| Сводка≠ | Value at Risk is a financial risk measure that estimates the maximum loss a position or portfolio could suffer over a fixed holding period at a given confidence level. It is the standard benchmark in risk management and regulatory capital calculations, developed in the textbook tradition of Jorion (2007) and the Basel market-risk framework. | 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). |
| ScholarGateНабор данных ↗ |
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