英文标题：
《Are credit ratings time-homogeneous and Markov?》
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作者：
Pedro Lencastre, Frank Raischel, Pedro G. Lind, Tim Rogers
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最新提交年份：
2014
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英文摘要：
We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody\'s and show that the validity of these assumptions - existence of a homogeneous generator and Markovianity - is not always guaranteed. Our analysis is based on a comparison between empirical transition matrices aggregated over fixed time windows and candidate transition matrices generated from measurements taken over shorter periods. Ratings are widely used in credit risk, and are a key element in risk assessment; our results provide a tool for quantifying confidence in predictions extrapolated from these time series.
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中文摘要：
我们介绍了一种简单的方法来测试同质发电机的可靠性和信用评级经验时间序列的随机过程的马尔可夫性质。我们分析了穆迪提供的开放获取数据，并表明这些假设的有效性——同质生成器和马尔可夫性的存在——并不总是得到保证。我们的分析基于在固定时间窗口内聚集的经验转移矩阵和在较短时间内进行的测量产生的候选转移矩阵之间的比较。评级广泛应用于信用风险，是风险评估的关键要素；我们的结果为量化从这些时间序列推断出的预测的可信度提供了工具。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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