Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks

Avishek Bhandari (), Ata Assaf () and Rajendra N. Paramanik ()
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Avishek Bhandari: Institute of Management Technology Hyderabad
Ata Assaf: University of Balamand, Lebanon and Cyprus International Institute of Management
Rajendra N. Paramanik: Indian Institute of Technology

A chapter in Studies in International Economics and Finance, 2022, pp 599-616 from Springer

Abstract: Abstract Despite several attempts in applied econometrics and time series literature to identify the common channels contributing to fractal structures and long memory in multivariate financial time series, we propose a wavelet-based fractal connectivity analysis, which is the first application in economics and financial studies, enabling one to successfully segregate markets into fractally similar or diverse groups and find that developed markets have similar fractal structures. Similarly, stock returns of emerging markets exhibiting long memory tend to follow similar fractal structures. Furthermore, network analyses of fractal connectivity support our findings on market efficiency which has theoretical roots in both fractal and adaptive market hypothesis.

Keywords: Long memory; Fractal connectivity; Wavelets; Hurst; Complex networks (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 C32 G15 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isbchp:978-981-16-7062-6_30

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DOI: 10.1007/978-981-16-7062-6_30

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