Multiscale online-horizontal-visibility-graph correlation analysis of financial market

Mengjiao Han, Qingju Fan and Guang Ling

Physica A: Statistical Mechanics and its Applications, 2022, vol. 607, issue C

Abstract: In this study, we introduce an analogous multiscale online-horizontal-visibility-graph correlation analysis (MOHVGCA) method to quantify the association strength between pairwise nonlinear time series. The new method is based on complex network approaches and proved to be a powerful and efficient tool for large-scale time series analysis. The versatility and robustness of this new method are illustrated through synthetic series. Results obtained allow us to be optimistic about its efficiency in correlation analysis of complex time series. Furthermore, we employ the new method to characterize the association between WTI Crude Oil futures and representative traded currencies on 5 min intra-day recordings in the period January 2013–December 2015. The results reveal that there exist positive associations between WTI Crude Oil and the considered traded currencies, and the association degree between WTI Crude Oil and currencies from oil-exporting and oil-importing countries is significantly different over different scales. The oil crisis weakens the association between oil and the currencies from the oil-importing currencies and the information exchange between oil and currencies from oil-exporting countries strengthened dramatically after the oil crisis. Even more, we detect that the Russian ruble always has the strongest ties with oil in different periods. These findings shed new light on the correlation analysis from the perspective of multiscale network analysis.

Keywords: Online horizontal visibility graph; Multiscale; Significance test; Association coefficient (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007531

DOI: 10.1016/j.physa.2022.128195

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