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Using the Autoregressive Conditional Duration Model to Analyze the Process of Default Contagion

机译:使用自回归条件持续时间模型来分析默认传染性的过程

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Credit events are not independent, and the contagion effect is very common. The seriousness of the contagion effect depends on the change in the default contagion duration before and after credit events. This study uses the Autoregressive Conditional Duration Model (ACD model) to capture the durations of a series of credit events and to study the characteristics of a default duration series. The empirical samples are listed and over-the-counter companies in Taiwan. The moving block bootstrap in LIU and Singh (1992) is employed to copy the sample data. The sample period is from October 1982 to December 2007. Our results show that, in the entire sample and subsamples of the electronic information industry and construction industry, the default duration series demonstrates the conditional autocorrelation and cluster effect. The ACD model helps capture the contagion effect of credit events.
机译:信用事件不是独立的,传染效果很常见。传染效应的严重性取决于信用事件前后默认传染期的变化。本研究使用自回归条件持续时间模型(ACD模型)来捕获一系列信用事件的持续时间,并研究默认持续时间系列的特性。经验样体上市和台湾的柜台上限。 LIU和SINGH(1992)中的移动块举自动启动将用于复制样本数据。样品期为1982年10月至2007年12月。我们的结果表明,在电子信息工业和建筑业的整个样本和副样本中,默认持续时间系列展示了条件自相关和集群效果。 ACD模型有助于捕获信用事件的传染效果。

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