Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines

Tom Duffy (), Manos Hatzakis (), Wenyue Hsu (), Russ Labe (), Bonnie Liao (), Xiangdong (Sheldon) Luo (), Je Oh (), Adeesh Setya () and Lihua Yang ()
Additional contact information
Tom Duffy: Merrill Lynch Global Bank Group, 34th Floor, 250 Vesey Street, New York, New York 10080
Manos Hatzakis: Merrill Lynch Management Science Group, PO Box 9065, Princeton, New Jersey 08543-9065
Wenyue Hsu: Merrill Lynch Bank USA, PO Box 9018, Princeton, New Jersey 08543-9018
Russ Labe: Merrill Lynch Management Science Group, PO Box 9065, Princeton, New Jersey 08543-9065
Bonnie Liao: Merrill Lynch Management Science Group, PO Box 9065, Princeton, New Jersey 08543-9065
Xiangdong (Sheldon) Luo: Merrill Lynch Bank USA, PO Box 9018, Princeton, New Jersey 08543-9018
Je Oh: Merrill Lynch Management Science Group, PO Box 9065, Princeton, New Jersey 08543-9065
Adeesh Setya: Merrill Lynch Bank USA, PO Box 9018, Princeton, New Jersey 08543-9018
Lihua Yang: Merrill Lynch Management Science Group, PO Box 9065, Princeton, New Jersey 08543-9065

Interfaces, 2005, vol. 35, issue 5, 353-369

Abstract: Merrill Lynch Bank USA has a multibillion dollar portfolio of revolving credit-line commitments with over 100 institutions. These credit lines give corporations access to a specified amount of cash for short-term funding needs. A key risk associated with credit lines is liquidity risk, or the risk that the bank will need to provide significant assets to the borrowers on short notice. We developed a Monte Carlo simulation to analyze liquidity risk of a revolving credit portfolio. The model incorporates a mix of OR/MS techniques, including a Markov transition process, expert-system rules, and correlated random variables to capture the impact of industry correlations among the borrowers. Results from the model enabled the bank to free up about $4 billion of liquidity. Over the 21 months since the bank implemented the model, the portfolio has expanded by 60 percent to over $13 billion. The model has become part of the bank’s tool kit for managing liquidity risk and continues to be used every month.

Keywords: financial institutions: banks; probability: Markov processes (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/inte.1050.0157 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:35:y:2005:i:5:p:353-369

Access Statistics for this article

More articles in Interfaces from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().