# A fix for failure that’s contagious

Applying ideas from diversification and cascading failures to mitigate the propagation of risk across inter-connected institutions.

Financial networks are like sprawling extended families. Their components are all interconnected, but they aren’t obviously dependent on each other until something goes wrong. Distress or failure in one area can propagate through the network via the very same avenues that made it seemingly strong and diverse.

This project captures the intricacies of financial systems using complex networks. By modelling the channels through which risk and stress in the network is communicated, it is possible to predict how different reactions to shocks will affect the network as a whole. Focusing on the microscopic theory of these networks, the DebtRank algorithm is studied as a tool for predicting the likelihood of systemic failure in a network, and appropriate immunisation strategies for limiting the spread of distress are proposed.

The sentiment of borrowers and lenders in a financial network is what drives markets to success, but also to ruin. Zooming in on the non-linear links between these players quantifies the likely reactions to key events, and predicts how distress will spread. Modelling these weaknesses will enable strategies to limit system-wide catastrophic failure to be tuned, and ultimately prevent future financial crashes.

## Related papers

### The interbank network

The large-scale structure of the interbank network changes drastically in times of crisis due to the effect of measures from central banks.

### Modelling financial systemic risk

Complex networks model the links between financial institutions and how these channels can transition from diversifying to propagating risk.

### Non-linear distress propagation

Non-linear models of distress propagation in financial networks characterise key regimes where shocks are either amplified or suppressed.

### Immunisation of systemic risk

Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.

### The price of complexity

Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.

### Cascades in flow networks

Coupled distribution grids are more vulnerable to a cascading systemic failure but they have larger safe regions within their networks.

### DebtRank and shock propagation

A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.

### Fragility of the interbank network

The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.

### Default cascades in networks

The optimal architecture of a financial system is only dependent on its topology when the market is illiquid, and no topology is always superior.

### Interbank controllability

Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.

### Bootstrapping topology and risk

Information about 10% of the links in a complex network is sufficient to reconstruct its main features and resilience with the fitness model.