Abstract
We develop a topology data analysis-based method to detect early signs for critical transitions in financial data. From the time-series of multiple stock prices, we build time-dependent correlation networks, which exhibit topological structures. We compute the persistent homology associated to these structures in order to track the changes in topology when approaching a critical transition. As a case study, we investigate a portfolio of stocks during a period prior to the US financial crisis of 2007–2008, and show the presence of early signs of the critical transition.
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Acknowledgements
Research of Marian Gidea was partially supported by the Alfred P. Sloan Foundation grant G-2016-7320, and by the NSF grant DMS-0635607.
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Gidea, M. (2017). Topological Data Analysis of Critical Transitions in Financial Networks. In: Shmueli, E., Barzel, B., Puzis, R. (eds) 3rd International Winter School and Conference on Network Science . NetSci-X 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-55471-6_5
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DOI: https://doi.org/10.1007/978-3-319-55471-6_5
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