银行经理整合模型来预测违约既需要有繁荣时期也需要有萧条时期通常发生的数据。
When bankers assembled models to predict defaults, they wanted data on what normally happened in both booms and busts.
研究在有噪音的信息环境下，银行如何利用结构化模型来预测违约概率问题。
This paper investigates how to predict default probability applying structural models when domestic Banks were faced with noisy information.
考虑到提前还款、违约风险等对现金流的影响，提出了对汽车金融资产支持证券的现金流预测模型。
Considered the influences of prepayment and breach risk, the article makes the model of the cash flow forecasting for Automobile Finance ABS.
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