Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation
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The construction of minimum spanning trees (MSTs) from correlation matrices is an often used method to study relationships in the financial markets. However most of the work on this topic tends to use the Pearson correlation coefficient, which relies on the assumption of normality and can be brittle to the presence of outliers, neither of which is ideal for the study of financial returns. In this paper we study the inference of MSTs from daily US financial returns using Pearson and two rank correlation methods, Spearman and Kendall's τ. We find that the trees constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation, that there are significant differences in the agreement of the centrality of various sectors and that despite these, the trees tend to have similar topologies.
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