Uncertainty and the Shadow Banking Crisis: Estimates from a Dynamic Model
SSRN, ISSN: 1556-5068
2017
- 4Citations
- 2,049Usage
- 14Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Shadow banks play an important role in the modern financial system and are arguably the source of key vulnerabilities that led to the 2007-2009 financial crisis. I develop a quantitative framework with uncertainty fluctuations and endogenous bank default to study the dynamics of shadow banking. I argue that the increase in asset return uncertainty during the crisis results in a spread spike, making it more costly for shadow banks to roll over their debt in the short-term debt market. As a result, these banks are forced to deleverage, leading to a decrease in credit intermediation. The model is estimated using a bank-level dataset of shadow banks in the United States. The parameter estimates imply that uncertainty shocks can explain 72% of asset contraction and 70% of deleveraging in the shadow banking sector. Maturity mismatch and asset fire-sales amplify the impact of the uncertainty shocks. First-moment shocks to bank asset return, financial shocks, or fire-sale cost shocks alone can not reproduce the large interbank spread spike, dramatic deleveraging or contraction in the U.S. shadow banking sector during the crisis. The model also allows for policy experiments. I analyze how unconventional monetary policies can help to counter the rise in the interbank spread, thus stabilizing the credit supply. Taking bank moral hazard into consideration, I find that government bailout might be counterproductive as it might result in more aggressive risk-taking among shadow banks, especially when bailout decisions are based on bank characteristics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85118021638&origin=inward; http://dx.doi.org/10.2139/ssrn.3091917; https://www.ssrn.com/abstract=3091917; https://dx.doi.org/10.2139/ssrn.3091917; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3091917; https://ssrn.com/abstract=3091917
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know