Long Memory in Stock Returns: Theory and Evidence

Gourishankar S. Hiremath ()
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Gourishankar S. Hiremath: Department of Humanities and Social Science, IIT Kharagpur

Chapter Chapter 5 in Indian Stock Market, 2014, pp 85-98 from Springer

Abstract: Abstract Long memory is a characteristic of a data generating process, in which autocorrelation function decays hyperbolically at a slower rate and the underlying time series realizations display significant temporal dependence at very distant observations. The issue of long memory though has important theoretical and practical implications, has not received due importance in India. The present chapter tests for the presence of long memory in mean of the stock returns by employing a set of semiparametric tests. A comprehensive data sample from June 1997 to March 2010 is used for the analysis. The findings of the study suggest the presence of long memory in mean returns. Furthermore, there are no significant and consistent evidence which could suggest that smaller indices are generally characterized by the long memory process. It implies a potential prediction of future returns over a longer period. The use of linear model in the presence of long memory would result in misleading inferences and this calls for further analysis of long memory forecasting models.

Keywords: ARFIMA; Autocovariance; Covariance stationary; Fractional integration; Hyperbolic decay; Long memory; Market efficiency; Semi-patrametric methods (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spbchp:978-81-322-1590-5_5

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DOI: 10.1007/978-81-322-1590-5_5

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