  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 [1] 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.
