1,086 research outputs found
Banking Panics and the Origin of Central Banking
Gorton and Huang (2001) argue that private coalitions of banks can act as central banks, issuing private money and providing deposit insurance during times of panic. This lender-of-last-resort role depends upon banking panics occurring threat of liquidation makes the private bank coalition incentive compatible, inducing banks to monitor each other. But, despite the evolution of private bank coalitions, government central banks and government deposit insurance schemes historically replaced the private bank coalitions. In this paper we ask why this transition from private arrangements to public arrangements occurred. We survey the historical and international evidence on panics, suggesting that Gorton and Huang (2001) are consistent with the evidence. Then, we extend Gorton and Huang (2001) to show the welfare improvement brought about by a government central bank replacing private bank coalitions as lender-of-last-resort. In particular, panics, while necessary for private coalitions to function, are costly because they disrupt the use of bank deposits as a medium of exchange. With government deposit insurance, panics do not occur, but the government must monitor banks. Such monitoring by the government is not as effective as private bank coalitions. We provide conditions under which the government can avoid the costs associated with panics by implementing deposit insurance and thereby raise social welfare.
Bank Panics and the Endogeneity of Central Banking
Central banking is intimately related to liquidity provision to banks during times of crisis, the lender-of-last-resort function. This activity arose endogenously in certain banking systems. Depositors lack full information about the value of bank assets so that during macroeconomic downturns they monitor their banks by withdrawing in a banking panic. The likelihood of panics depends on the industrial organization of the banking system. Banking systems with many small, undiversified banks, are prone to panics and failures, unlike systems with a few big banks that are heavily branched and well diversified. Systems of many small banks are more efficient if the banks form coalitions during times of crisis. We provide conditions under which the industrial organization of banking leads to incentive compatible state contingent bank coalition formation. Such coalitions issue money that is a kind of deposit insurance and examine and supervise banks. Bank coalitions of small banks, however, cannot replicate the efficiency of a system of big banks.
Liquidity, Efficiency and Bank Bailouts
Why do governments bailout banking systems in distress? We argue that the government can efficiently provide liquidity. We present a general equilibrium model in which not all assets can be used to purchase all other assets at every date. At some dates agents want to sell projects or securities. The only buyers are agents who have previously opportunistically invested in otherwise dominated assets because only these ( liquid') assets can be used to purchase the projects or securities. The market price of the projects or securities sold depends on the supply of liquidity, which is determined in general equilibrium. The supply of liquidity is not perfectly elastic so asset prices can deviate from efficient market' prices, that is, the conditional expectation of the asset payoff. While private liquidity provision is socially beneficial since it allows valuable reallocations, it is also socially costly since liquidity suppliers could have made more efficient investments ex ante. As a result, there is a potential role for the government to supply liquidity by issuing government securities, backed by tax revenue. Government bailouts of banking systems are an example of such public liquidity provision.
FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System
In the Industrial Internet of Things (IoT), a large amount of data will be
generated every day. Due to privacy and security issues, it is difficult to
collect all these data together to train deep learning models, thus the
federated learning, a distributed machine learning paradigm that protects data
privacy, has been widely used in IoT. However, in practical federated learning,
the data distributions usually have large differences across devices, and the
heterogeneity of data will deteriorate the performance of the model. Moreover,
federated learning in IoT usually has a large number of devices involved in
training, and the limited communication resource of cloud servers become a
bottleneck for training. To address the above issues, in this paper, we combine
centralized federated learning with decentralized federated learning to design
a semi-decentralized cloud-edge-device hierarchical federated learning
framework, which can mitigate the impact of data heterogeneity, and can be
deployed at lage scale in IoT. To address the effect of data heterogeneity, we
use an incremental subgradient optimization algorithm in each ring cluster to
improve the generalization ability of the ring cluster models. Our extensive
experiments show that our approach can effectively mitigate the impact of data
heterogeneity and alleviate the communication bottleneck in cloud servers.Comment: 11 pages, 10 figure
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