A Model for Screening Vulnerability in the Loan Market in the Context of Credit Rationing

Saeed Asadi Bagloee, Mohsen Asadi and Cyrus Mohebbi
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Saeed Asadi Bagloee: PARSONS, Dubai, United Arab Emirates
Mohsen Asadi: Kharazmi University, Tehran, Iran
Cyrus Mohebbi: Department of Information, Operation and Management Science, Kaufman Management Center, New York University, Columbia University, New York, NY, USA; Morgan Stanley, Purchase, USA

International Journal of Strategic Decision Sciences (IJSDS), 2014, vol. 5, issue 1, 59-75

Abstract: The loan market has contributed to the success and failure of economies. Examples of such failures are the US subprime mortgage crisis as well as the global economic meltdown that followed. Many factors influence the loan market, making it volatile and vulnerable. As such, it is important to understand the extent of its vulnerability. Such uncertainties emerge from asymmetric information in the loan market that may lead to credit rationing. Many studies have been devoted to exploring theoretical aspects of the credit market. However, before delving into the theory, it is important to understand and analyze empirical data. Having said that, the literature has yet to provide reliable methodologies for analyzing the empirical data of the loan market. Therefore, given an empirical survey, this study provides a model describing borrowers' behavior in the loan markets. Borrowers are faced with a variety of loan contracts with different terms and conditions from different banks. Logit models can be used to capture the borrowers' choice of bank. Credit is not easily available rather it is rationed and borrowers compete to obtain their required credit via best suited banks offers. The competition is guaranteed by developing a mathematical programming formulation (an objective function subject to constraints) integrated with the logit models for which a solution algorithm using Successive Coordinate Descent was developed. Numerical results of the methodology are presented. Loan terms and conditions as well the borrowers characteristics and preferences are captured in the logit models as explanatory variables. The methodology allows sensitivity analysis on the explanatory variables demonstrating the fluctuation and vulnerability of credit flow.

Date: 2014
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