Self-Affinity in Financial Asset Returns

Posted: 2 Jul 2012

See all articles by Enrico Onali

Enrico Onali

University of Exeter Business School

John Goddard

Bangor University - Bangor University

Date Written: July 2, 2012

Abstract

We test for departures from normal and independent and identically distributed (NIID) log returns, for log returns under the alternative hypothesis that are self-affine and either long-range dependent, or drawn randomly from an L-stable distribution with infinite higher-order moments. The finite sample performance of estimators of the two forms of self-affinity is explored in a simulation study. In contrast to rescaled range analysis and other conventional estimation methods, the variant of fluctuation analysis that considers finite sample moments only is able to identify both forms of self-affinity. When log returns are self-affine and long-range dependent under the alternative hypothesis, however, rescaled range analysis has higher power than fluctuation analysis. The techniques are illustrated by means of an analysis of the daily log returns for the indices of 11 stock markets of developed countries. Several of the smaller stock markets by capitalization exhibit evidence of long-range dependence in log returns.

Suggested Citation

Onali, Enrico and Goddard, John, Self-Affinity in Financial Asset Returns (July 2, 2012). International Review of Financial Analysis, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2097453

Enrico Onali

University of Exeter Business School ( email )

Exeter
United Kingdom

John Goddard (Contact Author)

Bangor University - Bangor University ( email )

Bangor, Wales LL57 2DG
United Kingdom

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