Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science.
Geraci, Marco Valerio
Public Library of Science (PLoS)
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Gandica, Y., Geraci, M. V., Béreau, S., & Gnabo, J. (2018). Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science.. PloS one, 13 (4), e0195110. https://doi.org/10.1371/journal.pone.0195110
Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Following  a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of $155$ financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poor's $500$ index over the period $1993 - 2014$. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network science's tools are able to support well known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers' collapse. More importantly, by means of less traditional metrics, such as sectoral interface or measurements based on contagion processes, our results document the co-existence of both fragmentation and integration phases between firms independently from the sector they belong to, and in doing so, question the relevance of existing macroprudential surveillance frameworks which have been mostly developed on a sectoral basis. Overall, our results improve our understanding of the US financial landscape and may have important implications for risk monitoring as well as macroprudential policy design.
Humans, Models, Economic, Algorithms, Industry, United States
Institute for New Economic Thinking (INET) (unknown)
External DOI: https://doi.org/10.1371/journal.pone.0195110
This record's URL: https://www.repository.cam.ac.uk/handle/1810/279474
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/