So sánh phương pháp

	Conditional Value-at-Risk (Expected Shortfall)	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Exponential GARCH (EGARCH)	Biến động thực hiện và Mô hình HAR
Lĩnh vực	Tài chính	Kinh tế lượng	Kinh tế lượng	Tài chính
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	2000	2015	1991	2009
Người khởi xướng	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Box & Jenkins (Box-Jenkins methodology)	Nelson	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
Loại	Coherent tail-risk measure	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Time-series regression of realized variance
Công trình gốc	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	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.
Tên gọi khác	CVaR, expected shortfall, average value-at-risk, tail VaR	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
Liên quan	5	5	4	5
Tóm tắt	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).	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.