Spatial Autocorrelation of Global Stock Exchanges Using Functional Areal Spatial Principal Component Analysis Article Swipe
Tzung Hsuen Khoo
,
Dharini Pathmanathan
,
Sophie Dabo‐Niang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11030674
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11030674
This work focuses on functional data presenting spatial dependence. The spatial autocorrelation of stock exchange returns for 71 stock exchanges from 69 countries was investigated using the functional Moran’s I statistic, classical principal component analysis (PCA) and functional areal spatial principal component analysis (FASPCA). This work focuses on the period where the 2015–2016 global market sell-off occurred and proved the existence of spatial autocorrelation among the stock exchanges studied. The stock exchange return data were converted into functional data before performing the classical PCA and FASPCA. Results from the Monte Carlo test of the functional Moran’s I statistics show that the 2015–2016 global market sell-off had a great impact on the spatial autocorrelation of stock exchanges. Principal components from FASPCA show positive spatial autocorrelation in the stock exchanges. Regional clusters were formed before, after and during the 2015–2016 global market sell-off period. This work explored the existence of positive spatial autocorrelation in global stock exchanges and showed that FASPCA is a useful tool in exploring spatial dependency in complex spatial data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11030674
- https://www.mdpi.com/2227-7390/11/3/674/pdf?version=1674957620
- OA Status
- gold
- Cited By
- 12
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318485586
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- OpenAlex ID
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https://openalex.org/W4318485586Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math11030674Digital Object Identifier
- Title
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Spatial Autocorrelation of Global Stock Exchanges Using Functional Areal Spatial Principal Component AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-28Full publication date if available
- Authors
-
Tzung Hsuen Khoo, Dharini Pathmanathan, Sophie Dabo‐NiangList of authors in order
- Landing page
-
https://doi.org/10.3390/math11030674Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-7390/11/3/674/pdf?version=1674957620Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/11/3/674/pdf?version=1674957620Direct OA link when available
- Concepts
-
Spatial analysis, Autocorrelation, Principal component analysis, Statistic, Econometrics, Functional data analysis, Spatial econometrics, Stock (firearms), Stock market, Stock exchange, Spatial dependence, Statistics, Geography, Economics, Mathematics, Finance, Context (archaeology), ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
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2025: 5, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_topic.display_name | Spatial and Panel Data Analysis |
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