手法を比較

	条件付きバリュー・アット・リスク（期待ショートフォール）	指数 GARCH (EGARCH)	実現ボラティリティとHARモデル
分野	ファイナンス	計量経済学	ファイナンス
系統	Regression model	Regression model	Regression model
提唱年	2000	1991	2009
提唱者	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Nelson	Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)
種類	Coherent tail-risk measure	Conditional volatility model (asymmetric GARCH variant)	Time-series regression of realized variance
原典	Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.	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.
別名	CVaR, expected shortfall, average value-at-risk, tail VaR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV
関連	5	4	5
概要	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.	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.