期刊
JOURNAL OF BANKING & FINANCE
卷 29, 期 5, 页码 1213-1236出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jbankfin.2004.04.008
关键词
default prediction; ROC analysis; model validation; rating systems; economic value of information; discrete choice models; model power; simulation; bank lending
In evaluating credit risk models, it is common to use metrics such as power curves and their associated statistics. However, power curves are not necessarily easily linked intuitively to common lending practices. Bankers often request a specific rule for defining a cut-off above which credit will be granted and below which it will be denied. In this paper we provide some quantitative insight into how such cut-offs can be developed. This framework accommodates real-world complications (e.g., relationship clients). We show that the simple cut-off approach can be extended to a more complete pricing approach that is more flexible and more profitable. We demonstrate that in general more powerful models are more profitable than weaker ones and we provide a simulation example. We also report results of another study that conservatively concludes a mid-sized bank might generate additional profits on the order of about $4.8 million per year after adopting a moderately more powerful model. (C) 2004 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据