...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of financial economics >The failure of models that predict failure: Distance, incentives, and defaults
【24h】

The failure of models that predict failure: Distance, incentives, and defaults

机译：预测失败的模型的失败：距离，激励和违约

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Statistical default models, widely used to assess default risk, fail to account for a change in the relations between different variables resulting from an underlying change in agent behavior. We demonstrate this phenomenon using data on securitized subprime mortgages issued in the period 1997-2006. As the level of securitization increases, lenders have an incentive to originate loans that rate high based on characteristics that are reported to investors, even if other unreported variables imply a lower borrower quality. Consistent with this behavior, we find that over time lenders set interest rates only on the basis of variables that are reported to investors, ignoring other credit-relevant information. As a result, among borrowers with similar reported characteristics, over time the set that receives loans becomes worse along the unreported information dimension. This change in lender behavior alters the data generating process by transforming the mapping from observables to loan defaults. To illustrate this effect, we show that the interest rate on a loan becomes a worse predictor of default as securitization increases. Moreover, a statistical default model estimated in a low securitization period breaks down in a high securitization period in a systematic manner: it underpredicts defaults among borrowers for whom soft information is more valuable. Regulations that rely on such models to assess default risk could, therefore, be undermined by the actions of market participants. (c) 2014 Elsevier B.V. All rights reserved.
机译：广泛用于评估违约风险的统计违约模型无法解释由行为主体的根本变化导致的不同变量之间关系的变化。我们使用1997年至2006年期间发行的证券化次级抵押贷款的数据来证明这种现象。随着证券化水平的提高，即使其他未报告的变量暗示了借款人的质量较差，贷方也有动机根据向投资者报告的特征发起高评级的贷款。与此行为一致，我们发现随着时间的流逝，放贷者仅根据报告给投资者的变量来设置利率，而忽略了其他与信贷有关的信息。结果，在具有类似报告特征的借款人中，随着时间的流逝，沿着未报告的信息维度，获得贷款的集合变得更糟。贷方行为的这种变化通过将映射从可观察到的债务违约转换为数据生成过程。为了说明这种影响，我们表明，随着证券化的增加，贷款利率成为违约的更坏预测指标。此外，在低证券化期间估算的统计违约模型会在系统化的高证券化期间失效：它低估了软信息更有价值的借款人的违约。因此，依赖此类模型评估违约风险的法规可能会被市场参与者的行为所破坏。 （c）2014 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号