The computation of time-varying long memory Hurst parameter for global stock markets

Yi, Chin Zi; Yeping, Yuan; Cheong, Chin Wen; Min, Lim

doi:10.47974/JSMS-1487

Open Access Research Article

The computation of time-varying long memory Hurst parameter for global stock markets

Chin Zi YiMAT2204274@xmu.edu.myDepartment of Mathematics School of Mathematics and Physics Xiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , Yuan YepingMAT2109475@xmu.edu.myDepartment of Mathematics School of Mathematics and Physics Xiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , ^*Chin Wen CheongCorresponding authorwcchin@xmu.edu.myDepartment of Mathematics School of Mathematics and Physics Xiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile → , Lim Minlimmin@xmu.edu.myDepartment of Mathematics School of Mathematics and Physics Xiamen University MalaysiaSepang, Selangor Darul Ehsan, 43900, MalaysiaView full profile →

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pp. 151–166Vol. 29Issue 2February 2026DOI: 10.47974/JSMS-1487XML

Received:: 03 Dec 2024
Published Online:: 13 Oct 2025
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1487
Pages:: 151–166

Abstract

This research paper focuses on examining the behaviour of stock markets and investigating whether global stocks exhibit the property of long memory. Several methodologies, such as aggregated variance, rescaled-range analysis, and the periodogram, are employed to test this hypothesis. The criteria for determining the presence of long memory involve the point estimation of the Hurst exponent. Beyond point estimation, the return series and volatility series are further analysed using rolling samples to observe the dynamic nature of long memory over the studied period. Our findings suggest that market behaviour evolves over time, demonstrating that a single static Hurst exponent is insufficient to assess the overall efficiency of the market.

Keywords

Financial time series Long memory Hurst exponent Volatility

Subject Classifications

91B1591B84

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