Yagufarov, Ruslan (2026): Two-scale topological momentum and persistence of stress regimes in correlation networks: evidence from equity markets.
Preview |
PDF
MPRA_paper_129341.pdf Download (3MB) | Preview |
Abstract
We apply persistent homology to the time-varying correlation network of 49 Fama –French industry portfolios (1976–2026) to study synchronization transitions associated with market stress. Vietoris–Rips filtrations on rolling Mantegna distance matrices capture one-dimensional homological cycles (H1) that are associated with intransitive triples of sectors—configurations where two pairwise correlations are strong while the third remains relatively weak. The analysis reveals a two-scale topological structure:stress episodes amplify intransitivity among the most strongly correlated industries while dissolving it among weakly and moderately correlated ones. Because static topological indicators are highly collinear with average correlation, we shift attention to the momentum of topological reorganization. Using strict temporal separation and moving-block bootstrap validation, we find that the standardized rate of change in the persistence-weighted mean cycle birth parameter provides information beyond standard synchronization metrics, improving forecasts of stress onset. Decomposing cycles into sectoral triples maps abstract topology onto interpretable sectoral linkages. The method avoids look-ahead bias and applies to other domains with time-varying correlation networks.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Two-scale topological momentum and persistence of stress regimes in correlation networks: evidence from equity markets |
| English Title: | Two-scale topological momentum and persistence of stress regimes in correlation networks: evidence from equity markets |
| Language: | English |
| Keywords: | persistent homology, topological data analysis, financial correlation networks, market stress, synchronization transitions, Vietoris-Rips filtration, Fama-French industry portfolios, out-of-sample forecasting, regime detection, systemic risk |
| Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
| Item ID: | 129341 |
| Depositing User: | Ruslan Yagufarov |
| Date Deposited: | 12 Jun 2026 12:18 |
| Last Modified: | 12 Jun 2026 12:18 |
| References: | R.N. Mantegna, Hierarchical structure in financial markets, Eur. Phys. J. B 11 (1999) 193--197. https://doi.org/10.1007/s100510050929 M. Tumminello, F. Lillo, R.N. Mantegna, Correlation, hierarchies, and networks in financial markets, J. Econ. Behav. Organ. 75 (2010) 40--58. https://doi.org/10.1016/j.jebo.2010.01.004 P.T.W. Yen, S.A. Cheong, Understanding changes in the topology and geometry of financial market correlations during a market crash, Entropy 23 (2021) 1211. https://doi.org/10.3390/e23091211 M. Billio, M. Getmansky, A.W. Lo, L. Pelizzon, Econometric measures of connectedness and systemic risk in the finance and insurance sectors, J. Financ. Econ. 104 (2012) 535--559. https://doi.org/10.1016/j.jfineco.2011.12.010 M. Kritzman, Y. Li, S. Page, R. Rigobon, Principal components as a measure of systemic risk, J. Portf. Manag. 37 (2011) 112--126. https://doi.org/10.3905/jpm.2011.37.4.112 L. Laloux, P. Cizeau, J.-P. Bouchaud, M. Potters, Noise dressing of financial correlation matrices, Phys. Rev. Lett. 83 (1999) 1467--1470. https://doi.org/10.1103/PhysRevLett.83.1467 H. Edelsbrunner, J.L. Harer, Computational Topology: An Introduction, American Mathematical Society, Providence, RI, 2010. G. Carlsson, Topology and data, Bull. Am. Math. Soc. 46 (2009) 255--308. https://doi.org/10.1090/S0273-0979-09-01249-X M. Gidea, Y. Katz, Topological data analysis of financial time series: Landscapes of crashes, Physica A 491 (2018) 820--834. https://doi.org/10.1016/j.physa.2017.09.033 M. Gidea, Topological data analysis of critical transitions in financial networks, in: V. Krishnamurthy, I.V. Tobor, A.H. Sayed (Eds.), Dynamic Data-Driven Environmental Systems Science, Springer, Cham, 2017, pp. 47--59. https://doi.org/10.1007/978-3-319-68765-0-5 D.Y. Kenett, M. Tumminello, A. Madi, G. Gur-Gershgoren, R.N. Mantegna, E. Ben-Jacob, Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market, PLoS ONE 5 (2010) e15032. https://doi.org/10.1371/journal.pone.0015032 D. Cohen-Steiner, H. Edelsbrunner, J. Harer, Stability of persistence diagrams, Discrete Comput. Geom. 37 (2007) 103--120. https://doi.org/10.1007/s00454-006-1276-5 E.F. Fama, K.R. French, Industry costs of equity, J. Financ. Econ. 43 (1997) 153--193. https://doi.org/10.1016/S0304-405X(96)00896-3 J.D. Hamilton, A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica 57 (1989) 357--384. https://doi.org/10.2307/1912559 C. Maria, J.-D. Boissonnat, M. Glisse, M. Yvinec, The GUDHI library: Simplicial complexes and persistent homology, in: H. Hong, C. Yap (Eds.), Mathematical Software -- ICMS 2014, Springer, Berlin, Heidelberg, 2014, pp. 167--174. https://doi.org/10.1007/978-3-662-44199-2-28 N. Otter, M.A. Porter, U. Tillmann, P. Grindrod, H.A. Harrington, A roadmap for the computation of persistent homology, EPJ Data Sci. 6 (2017) 17. https://doi.org/10.1140/epjds/s13688-017-0109-5 G. Marti, F. Nielsen, M. Bińkowski, P. Donnat, A review of two decades of correlations, hierarchies, networks and clustering in financial markets, Prog. Artif. Intell. 10 (2021) 245--268. https://doi.org/10.1007/s41060-020-00242-4 M. Tumminello, T. Di Matteo, T. Aste, R.N. Mantegna, Correlation based networks of equity returns sampled at different time horizons, Eur. Phys. J. B 75 (2010) 203--211. https://doi.org/10.1140/epjb/e2010-00095-5 R.,S. Yagufarov, Code for ``Two-Scale Topological Momentum and Persistence of Stress Regimes in Correlation Networks'', Zenodo (2026). https://doi.org/10.5281/zenodo.20476047 |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/129341 |

