防止在流动性需求连续暴露的情况下出现挤兑

IF 1.9 3区 经济学 Q2 ECONOMICS Journal of Economic Dynamics & Control Pub Date : 2023-11-13 DOI:10.1016/j.jedc.2023.104789
Lukas Voellmy
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引用次数: 0

摘要

我研究了发行可要求债券的金融中介是否可以通过适当调整或限制大量赎回的支付来消除挤兑均衡。我研究了一个模型,在这个模型中,投资者随着时间的推移了解自己的流动性需求,并可能在知道自己未来的流动性需求之前先发制人地撤出。防止挤兑的困难在于,一方面,在超额赎回的情况下,对撤资实施临时限制可能是必要的,以避免代价高昂的资产清算,而另一方面,正是这种对撤资限制的前景可能诱使投资者先发制人地撤资。当且仅当清算成本不太高或投资者不太厌恶风险时,在没有挤兑均衡的情况下实施最佳配置是可能的。
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Preventing runs under sequential revelation of liquidity needs

I examine whether a financial intermediary issuing demandable debt can eliminate run equilibria by properly adjusting or restricting payouts in case of heavy redemptions. I study a model where investors learn their own liquidity needs over time and may withdraw preemptively before knowing their future liquidity needs. The difficulty in preventing runs is that, on the one hand, imposing temporary restrictions on withdrawals in case of excess redemptions may be necessary to avoid costly asset liquidations, while on the other hand, it is precisely the prospect of such restrictions on withdrawals that may induce investors to withdraw preemptively. Implementation of the first-best allocation without a run equilibrium is possible if and only if either liquidation costs are not too high or investors are not too risk averse.

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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
199
期刊介绍: The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.
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