Authors: Bücher, Axel
Segers, Johan
Title: Inference for heavy tailed stationary time series based on sliding blocks
Language (ISO): en
Abstract: The block maxima method in extreme value theory consists of fitting an extreme value distribution to a sample of block maxima extracted from a time series. Traditionally, the maxima are taken over disjoint blocks of observations. Alternatively, the blocks can be chosen to slide through the observation period, yielding a larger number of overlapping blocks. Inference based on sliding blocks is found to be more efficient than inference based on disjoint blocks. The asymptotic variance of the maximum likelihood estimator of the Fréchet shape parameter is reduced by more than 18%. Interestingly, the amount of the efficiency gain is the same whatever the serial dependence of the underlying time series: as for disjoint blocks, the asymptotic distribution depends on the serial dependence only through the sequence of scaling constants. The findings are illustrated by simulation experiments and are applied to the estimation of high return levels of the daily log-returns of the Standard & Poor's 500 stock market index.
Subject Headings: Apéry's constant
block maxima
Fréchet distribution
maximum likelihood estimator
Marshall-Olkin distribution
Pickands dependence function
return level
Issue Date: 2017
Appears in Collections:Sonderforschungsbereich (SFB) 823

Files in This Item:
File Description SizeFormat 
DP_1217_SFB823_Bücher_Segers.pdfDNB483.87 kBAdobe PDFView/Open

This item is protected by original copyright

All resources in the repository are protected by copyright.