We study a preferred equity infusion government program set to mitigate interbank contagion. Financial institutions are prone to insolvency risk channeled through the network of interbank debt and to the risk of bank runs. The government seeks to maximize, under budget constraints, the total net worth of the financial system or, equivalently, to minimize the dead-weight losses induced by bank runs. The government is assumed to have complete information on interbank debt. The problem of quantifying the optimal amount of infusions can be expressed as a convex combinatorial optimization problem, tractable when the set of banks eligible for intervention (core banks) is sufficiently, yet realistically, small.We find that no bank has an incentive to withdraw from the program, when the preferred dividend rate paid to the government is equal to the government's outside rate of return on the intervention budget. On the other hand, it may be optimal for the government to make infusions in a strict subset of core banks.

In this paper we present a methodology of model-based calibration of additional capital needed in an interconnected financial system to minimize potential contagion losses. Building on ideas from combinatorial optimization tailored to controlling contagion in case of complete information about an interbank network, we augment the model with three plausible types of fire sale mechanisms. We then demonstrate the power of the methodology on the euro area banking system based on a network of 373 banks. On the basis of an exogenous shock leading to defaults of some banks in the network, we find that the contagion losses and the policy authority's ability to control them depend on the assumed fire sale mechanism and the fiscal budget constraint that may or may not restrain the policy authorities from infusing money to halt the contagion. The modelling framework could be used both as a crisis management tool to help inform decisions on capital/liquidity infusions in the context of resolutions and precautionary recapitalisations or as a crisis prevention tool to help calibrate capital buffer requirements to address systemic risks due to interconnectedness.

We consider a stylized core-periphery financial network in which links lead to the creation of projects in the outside economy but make banks prone to contagion risk. The controller seeks to maximize, under budget constraints, the value of the financial system defined as the total amount of projects. Under partial information on interbank links, revealed in conjunction with the spread of contagion, the optimal control problem is shown to become a Markov decision problem. We determine the optimal intervention policy by using dynamic programming.Our numerical results show that the value of the system depends on the connectivity in a non-monotonous way: it first increases with connectivity and then decreases with connectivity. The maximum value attained depends critically on the budget of the controller. Moreover, we show that for highly connected systems, it is optimal to increase the rate of intervention in the peripheral banks rather than in core banks.

This paper presents a novel approach to investigate and model the network of euro area banks’ large exposures within the global banking system. Drawing on a unique dataset, the paper documents the degree of interconnectedness and systemic risk of the euro area banking system based on bilateral linkages. We develop a Contagion Mapping model fully calibrated with bank-level data to study the contagion potential of an exogenous shock via credit and funding risks. We find that tipping points shifting the euro area banking system from a less vulnerable state to a highly vulnerable state are a non-linear function of the combination of network structures and bank-specific characteristics.

The network pattern of financial linkages is important in many areas of banking and finance. Yet bilateral linkages are often unobserved, and maximum entropy serves as the leading method for estimating counterparty exposures. This paper proposes an efficient alternative that combines information-theoretic arguments with economic incentives to produce more realistic interbank networks that preserve important characteristics of the original interbank market. The method loads the most probable links with the largest exposures consistent with the total lending and borrowing of each bank, yielding networks with minimum density. When used in a stress-testing context, the minimum density solution overestimates contagion, whereas maximum entropy underestimates it. Using the two benchmarks side by side defines a useful range that bounds the cost of systemic stress present in the true interbank network when counterparty exposures are unknown.

This paper proposes a dynamic multi-agent model of a banking system with central bank. Banks optimize a portfolio of risky investments and riskless excess reserves according to their risk, return, and liquidity preferences. They are linked via interbank loans and face stochastic deposit supply. Evidence is provided that the central bank stabilizes interbank markets in the short-run only. Comparing different interbank network structures, it is shown that money-center networks are more stable than random networks. Systemic risk via contagion is compared to common shocks and it is shown that both forms of systemic risk require different optimal policy responses.