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Sample period selection and long term dependence : new evidence from the Dow Jones index

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/146616

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Title: Sample period selection and long term dependence : new evidence from the Dow Jones index
Related: Chaos, solitons, and fractals, Vol. 36, Issue 5, (2008), p.1126-1140
DOI: 10.1016/j.chaos.2006.08.013
Publisher: Elsevier
Date: 2008
FoR/RFCD Code(s): 010500 Mathematical Physics 010200 Applied Mathematics 010300 Numerical and Computational Mathematics
Author/Creator: Batten, Jonathan A
Author/Creator: Ellis, Craig A
Author/Creator: Fethertson, Thomas A
Description: This study employs the classical and modified rescaled adjusted range statistic (R/S statistic) to investigate the sensitivity of the long-term return anomaly observed on the Dow Jones Industrial Average (DJIA) to sample and method bias. Daily data from 1/1/1970 to 17/3/2004 is used with sub-periods identified based on sign shifts in the mean returns as well as the October 1987 crash. The return series are also filtered to accommodate autoregressive conditional heteroskedastic (ARCH) innovations and short-term dependencies. Hurst exponent and V-statistic values for each of the filtered series for the whole sample and sub-periods are estimated, while polynomial regression techniques are applied to plot the V-statistics. These plots show oscillating cycles of varying lengths. Overall, we find the null hypothesis of no long-term dependence is accepted for the whole sample and every sub-period using the modified rescaled range test, but not necessarily using the classical rescaled adjusted range test. The later test does, however, reveal episodes of both positive and negative dependence over the various sample periods, which have been reported by other researchers.
Description: 15 page(s)
Subject Keyword: 010500 Mathematical Physics
Subject Keyword: 010200 Applied Mathematics
Subject Keyword: 010300 Numerical and Computational Mathematics
Resource Type: journal article
Organisation: Macquarie University. Macquarie Graduate School of Management

Identifier: http://hdl.handle.net/1959.14/146616
Identifier: ISSN:0960-0779
Identifier: mq-rm-2007004770
Language: eng
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