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Statistics of heteroscedastic extremes
Author(s) -
Einmahl John H. J.,
Haan Laurens,
Zhou Chen
Publication year - 2016
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12099
Subject(s) - extreme value theory , asymptotic distribution , estimator , mathematics , heteroscedasticity , quantile , independent and identically distributed random variables , statistics , econometrics , random variable
Summary We extend classical extreme value theory to non‐identically distributed observations. When the tails of the distribution are proportional much of extreme value statistics remains valid. The proportionality function for the tails can be estimated non‐parametrically along with the (common) extreme value index. For a positive extreme value index, joint asymptotic normality of both estimators is shown; they are asymptotically independent. We also establish asymptotic normality of a forecasted high quantile and develop tests for the proportionality function and for the validity of the model. We show through simulations the good performance of the procedures and also present an application to stock market returns. A main tool is the weak convergence of a weighted sequential tail empirical process.