论文标题
信用迁移：发电机
Credit migration: Generating generators
论文作者
论文摘要
马尔可夫信用迁移模型是当今合理的标准工具，但是对其进行校准存在根本的困难。我们展示了如何使用简化的矩阵发生器来解决这些问题，并解释了为什么如果没有波动性信息，就无法进行风险中性校准。我们还展示了如何使用差异几何形状的基本思想来对校准稳定性进行一般推断。这是风险（2021年2月）发表的文章的较长版本。
Markovian credit migration models are a reasonably standard tool nowadays, but there are fundamental difficulties with calibrating them. We show how these are resolved using a simplified form of matrix generator and explain why risk-neutral calibration cannot be done without volatility information. We also show how to use elementary ideas from differential geometry to make general inferences about calibration stability. This the longer version of an article published by RISK (Feb 2021).
