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| Valeur à risque conditionnelle (Expected Shortfall)× | Modèle ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Domaine≠ | Finance | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2000 | 2015 |
| Auteur d'origine≠ | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) | Box & Jenkins (Box-Jenkins methodology) |
| Type≠ | Coherent tail-risk measure | Univariate time-series model |
| Source fondatrice≠ | Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41. 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 |
| Alias≠ | CVaR, expected shortfall, average value-at-risk, tail VaR | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | 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). |
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