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Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines

Author

Listed:
  • 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)

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.

Suggested Citation

  • Tom Duffy & Manos Hatzakis & Wenyue Hsu & Russ Labe & Bonnie Liao & Xiangdong (Sheldon) Luo & Je Oh & Adeesh Setya & Lihua Yang, 2005. "Merrill Lynch Improves Liquidity Risk Management for Revolving Credit Lines," Interfaces, INFORMS, vol. 35(5), pages 353-369, October.
    • Handle: RePEc:inm:orinte:v:35:y:2005:i:5:p:353-369
    DOI: 10.1287/inte.1050.0157
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    References listed on IDEAS

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    1. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    2. Anil K. Kashyap & Raghuram Rajan & Jeremy C. Stein, 2002. "Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit‐taking," Journal of Finance, American Finance Association, vol. 57(1), pages 33-73, February.
    3. Kanatas, George, 1987. "Commercial paper, bank reserve requirements, and the informational role of loan commitments," Journal of Banking & Finance, Elsevier, vol. 11(3), pages 425-448, September.
    4. Beverly Hirtle & Mark E. Levonian & Marc R. Saidenberg & Stefan Walter & David M. Wright, 2001. "Using credit risk models for regulatory capital: issues and options," Economic Policy Review, Federal Reserve Bank of New York, issue Mar, pages 19-36.
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    Cited by:

    1. Grundke, Peter & Kühn, André, 2020. "The impact of the Basel III liquidity ratios on banks: Evidence from a simulation study," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 167-190.
    2. Raj Nigam, 2008. "Structuring and Sustaining Excellence in Management Science at Merrill Lynch," Interfaces, INFORMS, vol. 38(3), pages 202-209, June.

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