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In this paper we propose a subsampling estimator for the distributionof statistics diverging at either known or unknown rates when the underlyingtime series is strictly stationary and strong mixing. Based on ourresults we provide a detailed discussion how to estimate extreme orderstatistics with dependent data and present two applications to assessingnancial market risk. Our method performs well in estimating Value atRisk and provides a superior alternative to Hill's estimator in...

A Subsampling Approach to Estimating the Distribution of Diversing Statistics with Application to Assessing Financial Market Risks