Department of Microbiology, University Malaya, Kuala Lumpur, Malaysia
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Global Stock Exchanges Spatial Autocorrelation Using Functional Areal Spatial Principal Component Analysis
Author(s): Richael Treeby*
The functional data displaying geographical dependency are the main focus of this work. Using the functional Moran's I statistic, classical
principal component analysis and functional areal spatial principal component analysis, the spatial autocorrelation of stock exchange returns for
exchanges in 69 countries was examined. This study focuses on the time when the global stock market sold off and established that there is spatial
autocorrelation among the stock exchanges under consideration. Prior to applying the technique, the stock exchange return data were transformed
into functional data. The sell-off in the world markets had a significant influence on the spatial autocorrelation of stock exchanges, according to
the results of the Monte Carlo test of the functional Moran's I statistics. Positive spatial autocorrelation is visible in the stock exchanges' principa.. Read More»
DOI:
10.37421/2375-4389.2022.10.386
Journal of Global Economics received 1931 citations as per Google Scholar report