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| 极值理论 (EVT)× | 条件在险价值（预期缺口）× | 已实现波动率与HAR模型× | |
|---|---|---|---|
| 领域 | 金融学 | 金融学 | 金融学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2001 | 2000 | 2009 |
| 提出者≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Rockafellar & Uryasev (2000); Acerbi & Tasche (2002) | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| 类型≠ | Tail / extreme-event model | Coherent tail-risk measure | Time-series regression of realized variance |
| 开创性文献≠ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 | 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 ↗ |
| 别名 | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold | CVaR, expected shortfall, average value-at-risk, tail VaR | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| 相关 | 5 | 5 | 5 |
| 摘要≠ | 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. | 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. |
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