Data and Codes for: Public Liquidity and Financial Crises
Principal Investigator(s): View help for Principal Investigator(s) Wenhao Li, University of Southern California
Version: View help for Version V1
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Data Analysis | 07/17/2024 06:03:PM | ||
Figures | 07/17/2024 06:04:PM | ||
Model | 07/17/2024 06:04:PM | ||
Model Generated Data | 07/17/2024 06:04:PM | ||
Raw Data | 08/08/2024 11:41:AM | ||
Tables | 07/17/2024 06:05:PM | ||
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application/pdf | 248.3 KB | 08/08/2024 02:09:PM |
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text/x-matlab | 997 bytes | 07/17/2024 02:03:PM |
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text/x-rsrc | 1.5 KB | 07/17/2024 02:03:PM |
Project Citation:
Li, Wenhao. Data and Codes for: Public Liquidity and Financial Crises. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-03-10. https://doi.org/10.3886/E194683V1
Project Description
Summary:
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This paper studies the equilibrium effect of public liquidity on financial crises. Banks borrow from households via insured deposits and partially runnable debt, and suffer endogenous funding withdrawals from households in crises. Holding public liquidity alleviates banks' liquidity problems. In equilibrium, a larger public liquidity supply reduces crisis severity and expands bank lending, but it crowds bank deposits and increases bank vulnerability to real shocks. The model quantitatively explains 40% of Treasury liquidity premium variations. Counterfactual analyses reveal that QE1 significantly improves output, 20 times larger than QE3. However, QE policies raise bank fragility against non-financial shocks such as COVID-19.
Funding Sources:
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None
Scope of Project
Subject Terms:
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finance;
time series;
macroeconomics
JEL Classification:
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E44 Financial Markets and the Macroeconomy
E58 Central Banks and Their Policies
G01 Financial Crises
G28 Financial Institutions and Services: Government Policy and Regulation
E44 Financial Markets and the Macroeconomy
E58 Central Banks and Their Policies
G01 Financial Crises
G28 Financial Institutions and Services: Government Policy and Regulation
Geographic Coverage:
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All
Time Period(s):
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1970 – 2016
Collection Date(s):
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2023 – 2023
Universe:
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Multiple finance and macroeconomic time series in the dataset.
Data Type(s):
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aggregate data
Collection Notes:
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Certain data are private and need access to Bloomberg terminal or WRDS database. Data access requirements are stated in the ReadMe file.
Methodology
Response Rate:
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Not an experiment
Data Source:
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Multiple data sources, including FRED, CBOE, Bloomberg, etc. See detailed descriptions of data sources in the ReadMe file.
Unit(s) of Observation:
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Monthly
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