方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 极值理论 (EVT)× | 指数 GARCH (EGARCH)× | 已实现波动率与HAR模型× | |
|---|---|---|---|
| 领域≠ | 金融学 | 计量经济学 | 金融学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 2001 | 1991 | 2009 |
| 提出者≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Nelson | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| 类型≠ | Tail / extreme-event model | Conditional volatility model (asymmetric GARCH variant) | 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 | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. 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 | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| 相关≠ | 5 | 4 | 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. | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | 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. |
| ScholarGate数据集 ↗ |
|
|
|