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| Теория на екстремните стойности (ТЕС)× | Модел ARIMA (Autoregressive Integrated Moving Average)× | Условна стойност при риск (Очаквано недостигане)× | |
|---|---|---|---|
| Област≠ | Финанси | Иконометрия | Финанси |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2001 | 2015 | 2000 |
| Създател≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Box & Jenkins (Box-Jenkins methodology) | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) |
| Тип≠ | Tail / extreme-event model | Univariate time-series model | Coherent tail-risk measure |
| Основополагащ източник≠ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 | 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 | Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41. DOI ↗ |
| Други названия≠ | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | CVaR, expected shortfall, average value-at-risk, tail VaR |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | 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. | 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). | Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV. |
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