Contagious Herding and Endogenous Network Formation in Financial Network

Contagious Herding and Endogenous Network Formation in Financial Network by Co-Pierre Georg via Barry Ritholtz


When banks choose similar investment strategies, the financial system becomes vulnerable to common shocks. Banks decide about their investment strategy ex-ante based on a private belief about the state of the world and a social belief formed from observing the actions of peers. When the social belief is strong and the financial network is fragmented, banks follow their peers and their investment strategies synchronize. This effect is stronger for less informative private signals. For endogenously formed interbank networks, however, less informative signals lead to higher network density and less synchronization. It is shown that the former effect dominates the latter.

Contagious Herding and Endogenous Network Formation in Financial Networks – Introduction

When a large number of financial intermediaries choose the same investment strategy (i.e. their portfolios are very similar) the financial system as a whole becomes vulnerable to ex-post common shocks.1 A case at hand is the financial crisis of 2007/2008 when many banks invested in mortgage backed securities in anticipation that the underlying mortgages, many of which being US sub-prime mortgages, would not simultaneously depreciate in value. Fatally, this assumption turned out to be incorrect, and systemic risk ensued. How could so many banks choose the wrong, i.e. non-optimal given the state of the world, investment strategy, although they carefully monitor both economic fundamentals and the actions of other banks?

This paper presents a model in which financial intermediaries herd ex-ante and synchronize their investment strategy on a state non-matching strategy despite informative private signals about the state of the world. In a countable number of time-steps n agents representing financial intermediaries (banks for short) choose one of two actions. There are two states of the world which are revealed at every point in time with a certain probability p. A bank’s action is either state-matching, in which case the bank receives a positive payoff if the state is revealed, or it is state-non-matching in which case the bank receives zero. Banks are connected to a set of peers in a financial network of mutual lines of credit resembling the interbank market.

They receive a private signal about the state of the world and observe the previous strategies of their peers (but not of other banks). Based on both observations, they form a belief about the state of the world and choose their action accordingly.

The model presented in this paper is in essence a simple model of Bayesian learning in social networks but deviates from the existing literature (e.g. Gale and Kariv (2003), Acemoglu et al. (2011)) along two dimensions. First, instead of observing the actions of one peer at a time, each time adjusting their strategy accordingly, I assume that agents average over their peers’ actions in the previous period.2 The underlying assumption is that banks cannot adjust their actions (i.e. their investment strategy) as fast as they receive information from their peers and thus have to aggregate over potentially large amounts of information.3 Second, I allow banks to endogenously choose their set of peers in an extension of the baseline model. Here the underlying assumption is that banks have limited resources and do not monitor the actions of all other banks, but only from a strategically chosen subset. They receive utility from being interconnected through a learning effect.

Banks trade-o benefits from this coinsurance with a potential counter-party risk from peers choosing a state non-matching action (i.e. ex-post interbank contagion), being short on liquidity to re-balance their portfolio and thus drawing on the credit line.4 Finally, banks suffer larger losses when they chose a state non-matching action and the financial network is more densely interconnected through an amplification effect occurring e.g. when many agents re-balance their portfolios simultaneously, thereby triggering a re-sale. The resulting endogenous network structure is pairwise stable in the sense of Jackson and Wollinsky (1996). The model is implemented as an agent-based model (ABM) of the financial system.


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