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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of International Money and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of International Money and Finance, 73, Part A, 2017 DOI: 10.1016/j.jimonfin.2017.02.001

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System stress testing of bank liquidity risk

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System stress testing of bank liquidity risk. / Pagratis, Spyros; Topaloglou, Nikolas; Tsionas, Mike.
In: Journal of International Money and Finance, Vol. 73, No. Part A, 05.2017, p. 22-40.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Pagratis, S, Topaloglou, N & Tsionas, M 2017, 'System stress testing of bank liquidity risk', Journal of International Money and Finance, vol. 73, no. Part A, pp. 22-40. https://doi.org/10.1016/j.jimonfin.2017.02.001

APA

Pagratis, S., Topaloglou, N., & Tsionas, M. (2017). System stress testing of bank liquidity risk. Journal of International Money and Finance, 73(Part A), 22-40. https://doi.org/10.1016/j.jimonfin.2017.02.001

Vancouver

Pagratis S, Topaloglou N, Tsionas M. System stress testing of bank liquidity risk. Journal of International Money and Finance. 2017 May;73(Part A):22-40. Epub 2017 Feb 8. doi: 10.1016/j.jimonfin.2017.02.001

Author

Pagratis, Spyros ; Topaloglou, Nikolas ; Tsionas, Mike. / System stress testing of bank liquidity risk. In: Journal of International Money and Finance. 2017 ; Vol. 73, No. Part A. pp. 22-40.

Bibtex

@article{ee8e9c6ad8324990b2d52a5fd570abad,
title = "System stress testing of bank liquidity risk",
abstract = "Abstract Using a stress test methodology for bank liquidity risk we estimate the aggregate liquidity shortfall in the U.S. commercial banking system at the height of 2007–09 crisis, identifying key sources of funding vulnerabilities and the dominant composition of liquid asset holdings against liquidity shocks. The largest liquidity shocks to the system are estimated in the first half of the crisis, in line with Acharya and Mora (2015). Large banks experience the largest liquidity shortfall in 2008:Q1 ($154 billion or 14% of total assets) and small banks in 2007:Q4 ($117 billion or 11% of total assets). The dominant funding vulnerability to the system stems from large time deposits, while government securities largely dominate other classes of liquid assets as liquidity backstop. The analysis draws on detailed bank-level data on balance sheet flows of funds and applies stochastic dominance efficiency methods to capture liquidity risk diversification effects across assets and liabilities.",
keywords = "Banks, Liquidity risk, Stochastic dominance efficiency",
author = "Spyros Pagratis and Nikolas Topaloglou and Mike Tsionas",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of International Money and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of International Money and Finance, 73, Part A, 2017 DOI: 10.1016/j.jimonfin.2017.02.001",
year = "2017",
month = may,
doi = "10.1016/j.jimonfin.2017.02.001",
language = "English",
volume = "73",
pages = "22--40",
journal = "Journal of International Money and Finance",
issn = "0261-5606",
publisher = "Elsevier BV",
number = "Part A",

}

RIS

TY - JOUR

T1 - System stress testing of bank liquidity risk

AU - Pagratis, Spyros

AU - Topaloglou, Nikolas

AU - Tsionas, Mike

N1 - This is the author’s version of a work that was accepted for publication in Journal of International Money and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of International Money and Finance, 73, Part A, 2017 DOI: 10.1016/j.jimonfin.2017.02.001

PY - 2017/5

Y1 - 2017/5

N2 - Abstract Using a stress test methodology for bank liquidity risk we estimate the aggregate liquidity shortfall in the U.S. commercial banking system at the height of 2007–09 crisis, identifying key sources of funding vulnerabilities and the dominant composition of liquid asset holdings against liquidity shocks. The largest liquidity shocks to the system are estimated in the first half of the crisis, in line with Acharya and Mora (2015). Large banks experience the largest liquidity shortfall in 2008:Q1 ($154 billion or 14% of total assets) and small banks in 2007:Q4 ($117 billion or 11% of total assets). The dominant funding vulnerability to the system stems from large time deposits, while government securities largely dominate other classes of liquid assets as liquidity backstop. The analysis draws on detailed bank-level data on balance sheet flows of funds and applies stochastic dominance efficiency methods to capture liquidity risk diversification effects across assets and liabilities.

AB - Abstract Using a stress test methodology for bank liquidity risk we estimate the aggregate liquidity shortfall in the U.S. commercial banking system at the height of 2007–09 crisis, identifying key sources of funding vulnerabilities and the dominant composition of liquid asset holdings against liquidity shocks. The largest liquidity shocks to the system are estimated in the first half of the crisis, in line with Acharya and Mora (2015). Large banks experience the largest liquidity shortfall in 2008:Q1 ($154 billion or 14% of total assets) and small banks in 2007:Q4 ($117 billion or 11% of total assets). The dominant funding vulnerability to the system stems from large time deposits, while government securities largely dominate other classes of liquid assets as liquidity backstop. The analysis draws on detailed bank-level data on balance sheet flows of funds and applies stochastic dominance efficiency methods to capture liquidity risk diversification effects across assets and liabilities.

KW - Banks

KW - Liquidity risk

KW - Stochastic dominance efficiency

U2 - 10.1016/j.jimonfin.2017.02.001

DO - 10.1016/j.jimonfin.2017.02.001

M3 - Journal article

VL - 73

SP - 22

EP - 40

JO - Journal of International Money and Finance

JF - Journal of International Money and Finance

SN - 0261-5606

IS - Part A

ER -