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.
| Original language | English |
|---|---|
| Pages (from-to) | 353-369 |
| Number of pages | 17 |
| Journal | Interfaces |
| Volume | 35 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 2005 |
Keywords
- Financial institutions: banks
- Probability: Markov processes
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