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英文文献：Counting Processes for Retail Default Modeling-计数过程为零售默认建模
英文文献作者：Nicholas M. Kiefer,C. Erik Larson
英文文献摘要：
Counting processes provide a very flexible framework for modeling discrete events occurring over time. Estimation and interpretation is easy, and links to more familiar approaches are at hand. The key is to think of data as "event histories," a record of times of switching between states in a discrete state space. In a simple case, the states could be default/non-default; in other models relevant for credit modeling the states could be credit scores or payment status (30 dpd, 60 dpd, etc.). Here we focus on the use of stochastic counting processes for mortgage default modeling, using data on high LTV mortgages. Borrowers seeking to finance more than 80% of a house's value with a mortgage usually either purchase mortgage insurance, allowing a first mortgage greater than 80% from many lenders, or use second mortgages. Are there differences in performance between loans financed by these different methods? We address this question in the counting process framework. In fact, MI is associated with lower default rates for both fixed rate and adjustable rate first mortgages.
计数过程为建模一段时间内发生的离散事件提供了一个非常灵活的框架。评估和解释很容易，并且可以在手边链接到更熟悉的方法。关键是将数据视为“事件历史”，即在离散状态空间中状态之间切换的时间记录。在简单的情况下，状态可以是默认/非默认;在其他与信用建模相关的模型中，状态可能是信用分数或支付状态(30 dpd, 60 dpd，等等)。在这里，我们关注的是使用高价值比抵押贷款的数据，对抵押贷款违约建模的随机计数过程的使用。如果借款人希望用抵押贷款提供超过房屋价值80%的资金，他们通常要么购买抵押贷款保险，允许第一次抵押贷款超过许多银行的80%，要么使用第二次抵押贷款。通过这些不同的方式融资的贷款在业绩上有差异吗?我们在计数过程框架中解决这个问题。事实上，MI对于固定利率和可调利率第一抵押贷款都与较低的违约率有关。
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