방법 비교

	극단값 이론 (Extreme Value Theory, EVT)	조건부 위험값(Expected Shortfall)	지수적 GARCH (EGARCH)
분야	재무학	재무학	계량경제학
계열	Regression model	Regression model	Regression model
기원 연도	2001	2000	1991
창시자	Coles (textbook treatment); McNeil, Frey & Embrechts	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)	Nelson
유형	Tail / extreme-event model	Coherent tail-risk measure	Conditional volatility model (asymmetric GARCH variant)
원전	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598	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.
별칭	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold	CVaR, expected shortfall, average value-at-risk, tail VaR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH
관련	5	5	4
요약	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.	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.