方法对比

	条件在险价值（预期缺口）	ARIMA（自回归积分滑动平均）模型	分位数回归	已实现波动率与HAR模型
领域	金融学	计量经济学	计量经济学	金融学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	2000	2015	1978	2009
提出者	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Box & Jenkins (Box-Jenkins methodology)	Koenker & Bassett	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
类型	Coherent tail-risk measure	Univariate time-series model	Conditional quantile regression	Time-series regression of realized variance
开创性文献	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	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	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
别名	CVaR, expected shortfall, average value-at-risk, tail VaR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
相关	5	5	5	5
摘要	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).	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	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.