Incentivizing Resilience in Financial Networks

When banks extend loans to each other, they generate a negative externality in the form of systemic risk. They create a network of interbank exposures by which they expose other banks to potential insolvency cascades. In this paper, we show how a regulator can use information about the financial network to devise a transaction-specific tax based on a network centrality measure that captures systemic importance. Since different transactions have different impact on creating systemic risk, they are taxed differently. We call this tax a Systemic Risk Tax (SRT). We use an equilibrium concept inspired by the matching markets literature to show analytically that this SRT induces a unique equilibrium matching of lenders and borrowers that is systemic-risk efficient, i.e. it minimizes systemic risk given a certain transaction volume. On the other hand, we show that without this SRT multiple equilibrium matchings exist, which are generally inefficient. This allows the regulator to effectively stimulate a `rewiring' of the equilibrium interbank network so as to make it more resilient to insolvency cascades, without sacrificing transaction volume. Moreover, we show that a standard financial transaction tax (e.g. a Tobin-like tax) has no impact on reshaping the equilibrium financial network because it taxes all transactions indiscriminately. A Tobin-like tax is indeed shown to have a limited effect on reducing systemic risk while it decreases transaction volume.Comment: 38 pages, 9 figure

Information theoretic description of the e-Mid interbank market: implications for systemic risk

We present an empirical analysis of the European electronic interbank market of overnight lending (e-MID) during the years 1999–2009. The main goal of the paper is to explain the observed changes of the cross-sectional dispersion of lending/borrowing conditions before, during and after the 2007–2008 subprime crisis. Unlike previous contributions, that focused on banks’ dependent and macro information as explanatory variables, we address the role of banks’ behaviour and market microstructure as determinants of the credit spreads

Quantifying preferential trading in the e-MID interbank market

Interbank markets allow credit institutions to exchange capital for purposes of liquidity management. These markets are among the most liquid markets in the financial system. However, liquidity of interbank markets dropped during the 2007-2008 financial crisis, and such a lack of liquidity influenced the entire economic system. In this paper, we analyze transaction data from the e-MID market which is the only electronic interbank market in the Euro Area and US, over a period of eleven years (1999-2009). We adapt a method developed to detect statistically validated links in a network, in order to reveal preferential trading in a directed network. Preferential trading between banks is detected by comparing empirically observed trading relationships with a null hypothesis that assumes random trading among banks doing a heterogeneous number of transactions. Preferential trading patterns are revealed at time windows of 3-maintenance periods. We show that preferential trading is observed throughout the whole period of analysis and that the number of preferential trading links does not show any significant trend in time, in spite of a decreasing trend in the number of pairs of banks making transactions. We observe that preferential trading connections typically involve large trading volumes. During the crisis, we also observe that transactions occurring between banks with a preferential connection occur at larger interest rates than the complement set - an effect that is not observed before the crisis

Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions

Since the 2007-2009 financial crisis, substantial academic effort has been dedicated to improving our understanding of interbank lending networks (ILNs). Because of data limitations or by choice, the literature largely lacks multiple loan maturities. We employ a complete interbank loan contract dataset to investigate whether maturity details are informative of the network structure. Applying the layered stochastic block model of Peixoto (2015) and other tools from network science on a time series of bilateral loans with multiple maturity layers in the Russian ILN, we find that collapsing all such layers consistently obscures mesoscale structure. The optimal maturity granularity lies between completely collapsing and completely separating the maturity layers and depends on the development phase of the interbank market, with a more developed market requiring more layers for optimal description. Closer inspection of the inferred maturity bins associated with the optimal maturity granularity reveals specific economic functions, from liquidity intermediation to financing. Collapsing a network with multiple underlying maturity layers or extracting one such layer, common in economic research, is therefore not only an incomplete representation of the ILN's mesoscale structure, but also conceals existing economic functions. This holds important insights and opportunities for theoretical and empirical studies on interbank market functioning, contagion, stability, and on the desirable level of regulatory data disclosure

Interbank tiering and money center banks

This paper provides evidence that interbank markets are tiered rather than flat, in the sense that most banks do not lend to each other directly but through money center banks acting as intermediaries. We capture the concept of tiering by developing a core-periphery model, and devise a procedure for tting the model to real-world networks. Using Bundesbank data on bilateral interbank exposures among 1800 banks, we find strong evidence of tiering in the German banking system. Econometrically, bank-specific features, such as balance sheet size, predict how banks position themselves in the interbank market. This link provides a promising avenue for understanding the formation of financial networks.Interbank market ; Banks and banking, Central - Germany

Interbank tiering and money center banks

Interbank markets are tiered rather than flat, in the sense that many banks do not lend to each other directly but through money center banks which act as intermediaries. This paper captures the notion of tiering by designing a core-periphery model and develops a procedure for fitting an empirical network to this model. We find strong evidence of tiering for the German banking system, using bilateral interbank exposures among 1,800 banks. Moreover, bank-specific features, such as bank size, help explain how banks position themselves in the interbank market, suggesting that models with heterogenous banks could help shed light on how financial networks are formed.Interbank market

The effects of interbank networks on efficiency and stability in a macroeconomic agent-based model

We develop a macroeconomic agent-based model that consists of firms, banks, unions and households who interact on labour, goods, credit and interbank markets. The model endogenises pricing decisions by firms, wage setting by unions and interest rate setting by banks on both firm and interbank lending. Banks also set leverage targets and precautionary liquidity buffers on the basis of internal risk models. Our model produces endogenous fluctuations driven by the pricing behaviour of firms and the wage setting behaviour of unions. Fluctuations lead to loan defaults which are exacerbated as lenders reduce lending and charge higher interest rates, inducing a credit crunch. We also study how making the inter-banking network more connected affects the key outcomes of the economy and find that while the flow of funds from surplus banks to firms can be increased, the latter effect is soon dominated by increasing instability in the real sector as firms default at higher rates. While the banking sector experiences fewer defaults as a whole, losses on the interbank market increase as a source of bank defaults

An agent-based approach to interbank market lending decisions and risk implications

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending market. We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 U.S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literature. A key finding of this study is that risk preferences at the individual bank level can lead to unique interbank market structures that are suggestive of the capacity with which the market responds to surprising shocks