Fragmentation, integration and macroprudential surveillance of the US financial industry: Insights from network science

Abstract

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. Relying on 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 & Poors’ 500 index over the 1993–2014 period. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network sciences’ 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 sectors they belong to, and 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.

A multilevel analysis of financial institutions’ systemic exposure from local and system-wide information

“…The sectoral entropy captures the diversity of sectors within the community. In the descriptive analysis of 20 , sectoral diversity was suggested to be a determinant systemic risk when computed as the global sector-interface. Our estimations based on a formal regression analysis confirm this feature.…”

“…This means that all the nodes included in the same community display similar values for this set of variables. Turning to the topological metrics we are considering, we account for a wide set of standard measures: in-degree centrality , out-degree centrality , betweenness centrality , clustering centrality , m-reach centrality , inverse m-reach centrality , in-Katz centrality , and out-Katz centrality (detailed explanations of the variables can be found in 20 ). To take advantage of historical changes in the network as well as auxiliary information on firms’ industries, we complete our analysis with a set of less conventional metrics.…”

“…Diebold and Yilmaz, Giudici and Parisi, Betz et al 17 – 19 performed similar studies. Gandica et al 20 document the presence of sub-structures within the network of densely connected nodes. Sub-networks across time in the US market were identified using a community detection algorithm.…”

A multilevel analysis to systemic exposure: insights from local and system-wide information

“…Further studies in the same spirit were performed in [14,15,16]. In a previous study ( [21]), the presence of sub-structures within the network of densely connected nodes have been documented . Using community detection algorithm, sub-networks across time in the US market were identified.…”

“…This means that all the nodes included in the same community display similar values for this set of variables. Turning to the topological metrics we are considering, we account for a wide set of standard metrics: In-degree centrality, Out-degree centrality, Betweenness centrality, Clustering centrality, 1 m-reach centrality, Inverse m-reach centrality, in-Katz centrality and Out-Katz centrality (detailed explanation of each variable can be found in [21]). To take advantage of historical changes in the network as well as auxiliary information on firms' industry, we complete our analysis with a set of less conventional metrics.…”