262
G. Marti et al.
– Reference [14] investigates the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. They find that all four sectors have
become highly interrelated over the past decade, likely increasing the level of
systemic risk.
– Reference [127] shows that Ricci curvature may serve as an indicator of fragility
in the context of financial networks.
– Reference [115] detects distinct correlation regimes between 1998 and 2013.
These correlation regimes have been significantly different since the financial
crisis of 2008 than they had been previously. Cluster tracking shows that asset
classes are now less separated. Correlation networks help the authors to identify
“risk-on” and “risk-off” assets.
– For authors in [100], the identification of market states based on correlation
matrices can be helpful to build an “early warning system” for financial markets.
This system could be implemented by comparing the current state to previous
similar states or monitoring rapid changes in the correlation structure.
– In [102], authors study the clusters’ composition evolution, and their persistence.
They observe that the clustering structure is quite stable in the early 2000s
becoming gradually less persistent before the unfolding of the 2007-2008 crisis.
The correlation structure eventually recovers persistence in the aftermath of the
crisis, settling up a new phase which is distinct from the pre-crisis structure one,
where the market structure is less related to industrial sector activity.
– Reference [63] finds that financial institutions which have, in the correlation
networks, greater node strength, larger node betweenness centrality, larger node
closeness centrality and larger node clustering coefficient tend to be associated
with larger systemic risk contributions.
– References [11, 135] discuss the detection of early-warning signals of the 2008
crisis via the analysis of the properties of interbank networks.
– Authors in [2] define the structural entropy of a network, which is simply the
entropy of the probability vector encoding the proportional size of the clusters
in the network. They propose to use structural entropy to monitor the structure
of correlation-based networks over time. They suggest this quantity could be
an early warning indicator of financial crises. They observe “a remarkably high
linear correlation between the new measure and the volatility of the assets’ prices
over time”.
– In [28], authors highlight that the underlying financial network required to study
systemic risk is only partially observable in general. They propose a method
to reconstruct such a network, i.e. to build a set of (directed and weighted)
dependencies among the constituents of a complex system.
• Risk management methods:
– In [42], authors design clusters that tend to be comonotonic in their extreme low
values: To avoid contagion in the portfolio during risky scenarios, an investor
should diversify over these clusters.
G. Marti et al.
– Reference [14] investigates the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. They find that all four sectors have
become highly interrelated over the past decade, likely increasing the level of
systemic risk.
– Reference [127] shows that Ricci curvature may serve as an indicator of fragility
in the context of financial networks.
– Reference [115] detects distinct correlation regimes between 1998 and 2013.
These correlation regimes have been significantly different since the financial
crisis of 2008 than they had been previously. Cluster tracking shows that asset
classes are now less separated. Correlation networks help the authors to identify
“risk-on” and “risk-off” assets.
– For authors in [100], the identification of market states based on correlation
matrices can be helpful to build an “early warning system” for financial markets.
This system could be implemented by comparing the current state to previous
similar states or monitoring rapid changes in the correlation structure.
– In [102], authors study the clusters’ composition evolution, and their persistence.
They observe that the clustering structure is quite stable in the early 2000s
becoming gradually less persistent before the unfolding of the 2007-2008 crisis.
The correlation structure eventually recovers persistence in the aftermath of the
crisis, settling up a new phase which is distinct from the pre-crisis structure one,
where the market structure is less related to industrial sector activity.
– Reference [63] finds that financial institutions which have, in the correlation
networks, greater node strength, larger node betweenness centrality, larger node
closeness centrality and larger node clustering coefficient tend to be associated
with larger systemic risk contributions.
– References [11, 135] discuss the detection of early-warning signals of the 2008
crisis via the analysis of the properties of interbank networks.
– Authors in [2] define the structural entropy of a network, which is simply the
entropy of the probability vector encoding the proportional size of the clusters
in the network. They propose to use structural entropy to monitor the structure
of correlation-based networks over time. They suggest this quantity could be
an early warning indicator of financial crises. They observe “a remarkably high
linear correlation between the new measure and the volatility of the assets’ prices
over time”.
– In [28], authors highlight that the underlying financial network required to study
systemic risk is only partially observable in general. They propose a method
to reconstruct such a network, i.e. to build a set of (directed and weighted)
dependencies among the constituents of a complex system.
• Risk management methods:
– In [42], authors design clusters that tend to be comonotonic in their extreme low
values: To avoid contagion in the portfolio during risky scenarios, an investor
should diversify over these clusters.