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dc.contributor.authorAssaf, Ataen_US
dc.description.abstractDespite 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.en_US
dc.subjectLong memoryen_US
dc.subjectFractal connectivityen_US
dc.subjectComplex networksen_US
dc.titleFractal connectivity networks of select stock returns exhibiting long range dependenceen_US
dc.typeConference Presentationen_US
dc.relation.conferenceVirtual Conference on International Macroeconomics and Finance. ( 27th of March, 2021 - Kerala, India)en_US
dc.contributor.affiliationFaculty of Business and Managementen_US Bandharien_US N. Paramaniken_US
dc.description.statusSubmitted for reviewen_US
Appears in Collections:Department of Business Administration
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