金融时序中异常数据挖掘算法设计及实证分析

中国管理科学 ›› 2004, Vol. ›› Issue (3): 7-11.

金融时序中异常数据挖掘算法设计及实证分析

杨虎, 李强

重庆大学数理学院, 重庆, 400044

收稿日期:2003-05-27 修回日期:2004-03-31 出版日期:2004-06-28 发布日期:2012-03-07

Linear Mining Algorithms Design for Outliers in Financial Time Series and its Authentic Proofs

YANG Hu, LI Qiang

College of Science, Chongqing University, Chongqing 400044, China

Received:2003-05-27 Revised:2004-03-31 Online:2004-06-28 Published:2012-03-07

摘要/Abstract

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。

关键词: 金融时间序列, 创新异常, 信息准则

Abstract: 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.

Key words: financial time series, innovation outliers, informational criterion

中图分类号:

F830

杨虎, 李强. 金融时序中异常数据挖掘算法设计及实证分析[J]. 中国管理科学, 2004, (3): 7-11.

YANG Hu, LI Qiang. Linear Mining Algorithms Design for Outliers in Financial Time Series and its Authentic Proofs[J]. Chinese Journal of Management Science, 2004, (3): 7-11.

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