Coarse Graining on Financial Correlation Networks

Mehmet Ali Balcı, Larissa M. Batrancea, Ömer Akgüller and Anca Nichita
Additional contact information
Mehmet Ali Balcı: Department of Mathematics, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey
Larissa M. Batrancea: Department of Business, Babeş-Bolyai University, 400174 Cluj-Napoca, Romania
Ömer Akgüller: Department of Mathematics, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey
Anca Nichita: Faculty of Economics, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania

Mathematics, 2022, vol. 10, issue 12, 1-16

Abstract: Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at different levels. However, few studies pay attention to these multi-part features of financial networks. In this study, we present a geometric coarse graining method based on Voronoi regions of a financial network. Rather than studying the dense structure of the network, we perform our analysis on the triangular maximally filtering of a financial network. Such filtered topology emerges as an efficient approach because it keeps local clustering coefficients steady and it underlies the network geometry. Moreover, in order to capture changes in coarse grains geometry throughout a financial stress, we study Haantjes curvatures of paths that are the farthest from the center in each of the Voronoi regions. We performed our analysis on a network representation comprising the stock market indices BIST (Borsa Istanbul), FTSE100 (London Stock Exchange), and Nasdaq-100 Index (NASDAQ), across three financial crisis periods. Our results indicate that there are remarkable changes in the geometry of coarse grains.

Keywords: financial networks; coarse graining; Voronoi regions; triangular maximally filtered graph; Haantjes curvature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/12/2118/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/12/2118/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:12:p:2118-:d:841647

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().