手法を比較

	極値理論 (EVT)	条件付きバリュー・アット・リスク（期待ショートフォール）
分野	ファイナンス	ファイナンス
系統	Regression model	Regression model
提唱年	2001	2000
提唱者	Coles (textbook treatment); McNeil, Frey & Embrechts	Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)
種類	Tail / extreme-event model	Coherent tail-risk measure
原典	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.
別名	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold	CVaR, expected shortfall, average value-at-risk, tail VaR
関連	5	5
概要	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.	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.