方法对比

	Importance Sampling	极值理论 (EVT)
领域	仿真	金融学
方法族	Process / pipeline	Regression model
起源年份	1951	2001
提出者	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	Coles (textbook treatment); McNeil, Frey & Embrechts
类型	Monte Carlo variance-reduction technique	Tail / extreme-event model
开创性文献	Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
别名	IS, weighted Monte Carlo, Önem Örneklemesi	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
相关	5	5
摘要	Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs.	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.