10 Correlations, Hierarchies, Networks and Clustering
263
– As far as diversification is concerned, portfolio managers should probably focus
on the most stable parts of the graph [78].
– In [99], authors postulate the existence of a hierarchical structure of risks which
can be deemed responsible for both stock multivariate dependency structure and
univariate multifractal behaviour, and then propose a model that reproduces the
empirical observations (entanglement of univariate multi-scaling and multivariate cross-correlation properties of financial time series). The interplay between
multi-scaling and average cross-correlation is confirmed in [23].
– Industries (e.g. clusters as statistical industry classification) can be used as risk
factors in multifactor risk models [68].
– Clusters (statistical industry classification) can be an alternative to sometimes
unavailable “fundamental” industry classifications (e.g. in emerging or small
markets) [68].
– In [10], authors apply the TMFG for building sparse forecasting models and for
financial applications such as stress-testing and risk allocation.
– Reference [83] predicts credit risk based on local properties of the network of
payments between firms.
We found that the risk literature using correlation networks and clusters consists
essentially in descriptive studies. For now, there are only too few propositions in the
academic literature to build effective network-based or cluster-based risk systems.
10.6.4 Financial Policy Making
Clusters and networks can help designing financial policies. Several papers propose to
leverage them to detect risky market environments, develop indicators that can predict
forthcoming crisis or economic recovery [155], improve economic nowcasting [43],
or find key markets and assets that drive a whole region, and on which stimulus can
be applied effectively.
• Authors of [57] claim that “separation prevents failure propagation and connections
increase risks of global crises” whereas the prevailing view in favor of deregulation
is that banks, by investing in diverse sectors, would have greater stability. To
support their argument, using financial networks, they study the aftermath of the
Glass-Steagall Act (1933) repeal by Clinton administration in 1999. They find
that erosion of the Glass–Steagall Act, and cross sector investments eliminated
“firewalls” that could have prevented the housing sector decline from triggering a
wider financial and economic crisis:
Our analysis implies that the investment across economic sectors itself creates increased
cross-linking of otherwise much more weakly coupled parts of the economy, causing
dependencies that increase, rather than decrease, risk.
• According to [14], bank and insurance capital requirements and risk management
practices based on VaR, which are intended to ensure the soundness of individ-
263
– As far as diversification is concerned, portfolio managers should probably focus
on the most stable parts of the graph [78].
– In [99], authors postulate the existence of a hierarchical structure of risks which
can be deemed responsible for both stock multivariate dependency structure and
univariate multifractal behaviour, and then propose a model that reproduces the
empirical observations (entanglement of univariate multi-scaling and multivariate cross-correlation properties of financial time series). The interplay between
multi-scaling and average cross-correlation is confirmed in [23].
– Industries (e.g. clusters as statistical industry classification) can be used as risk
factors in multifactor risk models [68].
– Clusters (statistical industry classification) can be an alternative to sometimes
unavailable “fundamental” industry classifications (e.g. in emerging or small
markets) [68].
– In [10], authors apply the TMFG for building sparse forecasting models and for
financial applications such as stress-testing and risk allocation.
– Reference [83] predicts credit risk based on local properties of the network of
payments between firms.
We found that the risk literature using correlation networks and clusters consists
essentially in descriptive studies. For now, there are only too few propositions in the
academic literature to build effective network-based or cluster-based risk systems.
10.6.4 Financial Policy Making
Clusters and networks can help designing financial policies. Several papers propose to
leverage them to detect risky market environments, develop indicators that can predict
forthcoming crisis or economic recovery [155], improve economic nowcasting [43],
or find key markets and assets that drive a whole region, and on which stimulus can
be applied effectively.
• Authors of [57] claim that “separation prevents failure propagation and connections
increase risks of global crises” whereas the prevailing view in favor of deregulation
is that banks, by investing in diverse sectors, would have greater stability. To
support their argument, using financial networks, they study the aftermath of the
Glass-Steagall Act (1933) repeal by Clinton administration in 1999. They find
that erosion of the Glass–Steagall Act, and cross sector investments eliminated
“firewalls” that could have prevented the housing sector decline from triggering a
wider financial and economic crisis:
Our analysis implies that the investment across economic sectors itself creates increased
cross-linking of otherwise much more weakly coupled parts of the economy, causing
dependencies that increase, rather than decrease, risk.
• According to [14], bank and insurance capital requirements and risk management
practices based on VaR, which are intended to ensure the soundness of individ-