作　　者： ; ;

机构地区： 重庆大学数理学院

出　　处： 《中国管理科学》 2004年第3期7-11,共5页

摘　　要： 金融市场中的数据由于其内在联系,通常表现为相互关联的时间序列。本文主要讨论如何将金融市场中时间序列模型简化为相应的线性模型,继而用传统的线性模型方法去检验异常值的存在,并且判断该异常值是加性异常值还是创新异常值。创新异常值的挖掘对于金融风险的研究不仅具有理论上的意义,而且具有很强的现实意义。最后进行了算法的实证分析,结果表明本文的两种方法在金融市场的研究中是可行的并且行之有效。 Owing to the internal relations,the data in financial market usually manifest as the interrelated time series.This paper mainly discusses how to simplify time series models in financial market into relevant linear models and how to examine the existence of outliers and differentiate innovation outliers from additive outliers with traditional linear models.The mining of innovation outliers has not only the theoretical significance but also a great practical significance in the research on financial risk.Besides,the two algorithms proposed in this paper are analyzed with authentic proofs;in this way,the two methods in the study of financial market are proved feasible and effective.

关 键 词： 金融时同序列 创新异常 信息准则

领　　域： [经济管理]