Being able to map contagion risks and interconnectedness could play an important role in helping regulators avert future financial crises, or at least ensure they are not as bad. By Justin Lyon, CEO at Simudyne.

Contagion risk is the possibility that a shock to one financial institution spills over to others. Even seemingly small shocks can have significant knock-on effects on other market participants, especially trading counterparties. Contagion is one of the primary dynamics driving systemic risk in financial markets. 

Markets by their very nature are a complex web of interconnections between the constituent parts of the financial system. They can act as pathways along which instability spreads and amplifies. 

They are also vastly complex adaptive systems. The market reacts and adapts to the behaviour of other market participants. 

During the financial crisis, fears about possible contagion led the US government to extend an $85bn bailout to US insurance giant American International Group (AIG). 

It was not so much the fact that AIG was too big to fail, but rather that it was too interconnected to fail. The amount of credit default swaps held by its counterparties used by other market participants was highly significant. Practically every large US financial institution was using AIG credit default swaps (CDS) to protect itself. CDS and the relationship between banks (where banks in essence are dependent upon one another) in this case acted as part of the transmission mechanism. The potential spillover was almost like a slow-moving explosion swallowing up the institutions in its path. 

The chain of events unleashed by the AIG default was hard to quantify. By one estimate, counterparties were on the hook for $180bn, potentially triggering more failures and thus amplifying that contagion around the world. 

Supply chains and correlation

A more recent example of contagion risk occurred when UK construction company Carillion collapsed, leaving thousands of suppliers with unfulfilled contracts. This set off waves of financial distress. Lenders who had lent, not only to Carillion, but also to its suppliers,  realised they were going to be hit with missed repayments twice :  first by Carillion, and then by its suppliers. 

Traditional correlation analysis may not pick this up. Without knowing that a firm was a supplier of Carillion, it was hard to detect a relationship between the two. In fact, understanding the dependencies at work here and transmissions mechanism was very difficult. 

Likewise, without knowledge of a financial network, it is impossible to uncover a causative contagion relationship, let alone interpret those relationships to establish an actionable set of mitigating decisions. Modelling at higher levels of aggregation while considering only correlations, rather than causative relationships, can miss important dynamics.

At the cutting edge

As balance sheets become more complicated, the web of interconnectivity has become increasingly tangled. In a globalised world, recognising that the financial system is a complex adaptive system allows us to account for the types of dynamic experienced during the crisis. 

Many regulators acknowledge they need better ways of assessing systemic risk as well as the stability or lack thereof of the financial system. One of the biggest obstacles is the inability to see through the opacity of complicated financial networks. Almost all regulators monitoring the health of our financial system find this very, very difficult. The tools and approaches they need are quite new and are still being put into practice

One regulator headed in the right direction is the Bank of England. It is putting financial networks at the heart of its thinking rather than individual institutions as isolated nodes. It recognises that institutions are part of a larger system, the properties of which emerge from the interaction of its constituent parts.

The Bank of England is building new models that explicitly capture low-level heterogeneity, and explicitly account for the interconnectivity between the constituent parts of the financial system. 

These so-called agent-based models for risk management help to capture causative relationships –  be they funding providers pulling funds from banks, CDS contracts linking financial institutions to failing insurers, or suppliers losing out on contracts from failed construction companies  –  that have the potential to do significant harm. 

Traditional statistical methods are valid, especially for business as usual, but it is that emergent behaviour they fail to capture – stressed or crisis scenarios – where they have little value in answering the most important question institutions must answer: what do we do next?

One day, regulation may become proactive and preventative: it could knowingly outline the path to properly mitigating or even avoiding crises altogether. So, where there was a deep financial crisis in the past, there might instead be a shallow ripple. Time will tell. 

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