Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.721400
Title: Three essays on macroprudential policy and learning
Author: Liu, Keqing
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2017
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Abstract:
The first chapter proposes an alternative macroprudential policy in the framework of Gertler, Kiyotaki and Queralto (2012). In their model, the central bank subsidizes bank outside equity, where the subsidy rate is determined by the shadow cost of the deposit. We find that the alternative rule in which the subsidy rate responds to the aggregate bank outside equity ratio is welfare improving because it has a better stabilization effect on the bank asset deterioration after a nancial shock. We disentangle different channels through which macroprudential policies affect the economy and demonstrate that the better stabilization in the post-crisis economy has a positive effect on the economy in normal times through security prices. In the second chapter, we consider a model where producers set their prices based on their prediction of the aggregated price level and an exogenous variable, which can be a demand or a cost-push shock. To form their expectations, they use OLS-type econometric learning with bounded memory. We show that the aggregated price follows the random coe cient autoregressive process and we prove that this process is covariance stationary. This chapter comments on Angeloni and Faia (2013, Journal of Monetary Economics), a dynamic stochastic general equilibrium model with a risky banking sector. We identify the sources of inefficiency in the model and disentangle the channels through which banks choose a high level of leverage. We explain that their assumptions that generate banks over-borrowing feature lead to the return on assets and the bankruptcy probability that are unrealistically high. Next, we modify the model by incorporating the banking sector of Gertler and Karadi (2011) into the AF model and show that the calibration result improves.
Supervisor: Igarashi, Yoske Sponsor: ESRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.721400  DOI: Not available
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