方法对比
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| 极值理论 (EVT)× | ARIMA（自回归积分滑动平均）模型× | 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 | 2015 | 2009 |
| 提出者≠ | Coles (textbook treatment); McNeil, Frey & Embrechts | Box & Jenkins (Box-Jenkins methodology) | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| 类型≠ | Tail / extreme-event model | Univariate time-series model | 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 | 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 | 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 | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | 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. | 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). | 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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