Classification of international stock markets through MDS based on Hurst-surface distance

Xiaoming Liu, Aijing Lin and Shuqi Li

Physica A: Statistical Mechanics and its Applications, 2021, vol. 566, issue C

Abstract: Classifying stock markets by measuring the similarity between them can provide a reliable reference for investors and help them earn more profits. This paper seeks to explore a better method to measure the degree of similarity between international stock markets. We chose the daily closing price of 24 stock markets from the Americas, Asia, Europe, and Australia, and mapped them into three-dimensional space as points. In order to measure the similarity, we propose using Hurst-surface distance as a transformation and then use this distance matrix as a dissimilarity matrix to classify the stocks by multidimensional scaling method(MDS). We compared the classified results with classical MDS which use the Euclidean distance as a measure of similarity and σDCCA-based MDS. The results show that using Hurst-surface distance as a reflection of similarity can not only provide more information about correlation but also distinguish the differences of economic volatility in different regions. While σDCCA puts more emphasis on the similarities and dissimilarities within the same region. Those two modified MDS technology above are better than the classical method based on Euclidean, they can reflect more detailed and clearer information.

Keywords: Financial time series; DCCA cross-correlation coefficient; Hurst exponent; Surface distance; Multidimensional scaling (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120308839

DOI: 10.1016/j.physa.2020.125585

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