Article ID: | iaor20062151 |
Country: | United States |
Volume: | 35 |
Issue: | 5 |
Start Page Number: | 353 |
End Page Number: | 369 |
Publication Date: | Sep 2005 |
Journal: | Interfaces |
Authors: | Labe Russ, Liao Bonnie, Oh Je, Duffy Tom, Hatzakis Manos, Hsu Wenyue, Luo Xiangdong (Sheldon), Setya Adeesh, Yang Lihua |
Keywords: | markov processes, probability, financial |
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.