arXiv:0902.4796 [math.PR]AbstractReferencesReviewsResources
A Berry--Esseen theorem for sample quantiles under weak dependence
Published 2009-02-27Version 1
This paper proves a Berry--Esseen theorem for sample quantiles of strongly-mixing random variables under a polynomial mixing rate. The rate of normal approximation is shown to be $O(n^{-1/2})$ as $n\to\infty$, where $n$ denotes the sample size. This result is in sharp contrast to the case of the sample mean of strongly-mixing random variables where the rate $O(n^{-1/2})$ is not known even under an exponential strong mixing rate. The main result of the paper has applications in finance and econometrics as financial time series data often are heavy-tailed and quantile based methods play an important role in various problems in finance, including hedging and risk management.
Comments: Published in at http://dx.doi.org/10.1214/08-AAP533 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Journal: Annals of Applied Probability 2009, Vol. 19, No. 1, 108-126
DOI: 10.1214/08-AAP533
Categories: math.PR
Keywords: sample quantiles, berry-esseen theorem, weak dependence, strongly-mixing random variables, financial time series data
Tags: journal article
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