Default cascades in complex networks: topology and systemic risk
The optimal architecture of a financial system is only dependent on its topology when the market is illiquid, and no topology is always superior.
T. Roukny, H. Bersini, H. Pirotte, G. Caldarelli, S. Battiston
The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.
More in Systemic risk
Complex networks model the links between financial institutions and how these channels can transition from diversifying to propagating risk.
Non-linear models of distress propagation in financial networks characterise key regimes where shocks are either amplified or suppressed.
Coupled distribution grids are more vulnerable to a cascading systemic failure but they have larger safe regions within their networks.
Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.
The large-scale structure of the interbank network changes drastically in times of crisis due to the effect of measures from central banks.
Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.
The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.
Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.
A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.
Information about 10% of the links in a complex network is sufficient to reconstruct its main features and resilience with the fitness model.