Uncertainty and the Shadow Banking Crisis: Estimates from a Dynamic Model

70 Pages Posted: 27 Dec 2017 Last revised: 7 Apr 2020

See all articles by Xu Tian

Xu Tian

University of Toronto

Date Written: January 1, 2019

Abstract

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.

Keywords: Shadow banking, uncertainty, maturity mismatch, fire-sale, unconventional monetary policy, moral hazard

JEL Classification: D81, E32, E44, E50, G18, G20

Suggested Citation

Tian, Xu, Uncertainty and the Shadow Banking Crisis: Estimates from a Dynamic Model (January 1, 2019). Available at SSRN: https://ssrn.com/abstract=3091917 or http://dx.doi.org/10.2139/ssrn.3091917

Xu Tian (Contact Author)

University of Toronto ( email )

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Toronto, Ontario M5S 3G7
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647-606-0709 (Phone)

HOME PAGE: http://www.xutianur.com

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