Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Условный риск (Expected Shortfall)× | Реализованная волатильность и модель HAR× | |
|---|---|---|
| Область | Финансы | Финансы |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2000 | 2009 |
| Автор метода≠ | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Тип≠ | Coherent tail-risk measure | Time-series regression of realized variance |
| Основополагающий источник≠ | Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Другие названия | CVaR, expected shortfall, average value-at-risk, tail VaR | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
| ScholarGateНабор данных ↗ |
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