Estimating long range dependence: finite sample properties and confidence intervals

Rafał Weron

No HSC/01/03, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Science and Technology

Abstract: A major issue in financial economics is the behavior of asset returns over long horizons. Various estimators of long range dependence have been proposed. Even though some have known asymptotic properties, it is important to test their accuracy by using simulated series of different lengths. We test R/S analysis, Detrended Fluctuation Analysis and periodogram regression methods on samples drawn from Gaussian white noise. The DFA statistics turns out to be the unanimous winner. Unfortunately, no asymptotic distribution theory has been derived for this statistics so far. We were able, however, to construct empirical (i.e. approximate) confidence intervals for all three methods. The obtained values differ largely from heuristic values proposed by some authors for the R/S statistics and are very close to asymptotic values for the periodogram regression method.

Keywords: Long-range dependence; Hurst exponent; R/S analysis; Detrended Fluctuation Analysis; Periodogram regression; Confidence interval (search for similar items in EconPapers)
JEL-codes: C12 C13 C46 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2001
References: View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Published in Physica A 312 (2002) 285-299.

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http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_01_03.pdf Final draft, 2001 (application/pdf)
http://dx.doi.org/10.1016/S0378-4371(02)00961-5 Final printed version, 2002 (text/html)
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Journal Article: Estimating long-range dependence: finite sample properties and confidence intervals (2002)
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