parametricandnonparametriccombinationmodeltoenhanceoverallperformanceondefaultprediction

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	parametricandnonparametriccombinationmodeltoenhanceoverallperformanceondefaultprediction
	Li Jun 2; Pan Liang 3; Chen Muzi 1; Yang Xiaoguang4
刊名	journalofsystemsscienceandcomplexity
	2014
卷号	27 期号:5 页码:950
ISSN号	1009-6124
英文摘要	The probability of default (PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management. To obtain good PD estimation, practitioners and academics have put forward numerous default prediction models. However, how to use multiple models to enhance overall performance on default prediction remains untouched. In this paper, a parametric and non-parametric combination model is proposed. Firstly, binary logistic regression model (BLRM), support vector machine (SVM), and decision tree (DT) are used respectively to establish models with relatively stable and high performance. Secondly, in order to make further improvement to the overall performance, a combination model using the method of multiple discriminant analysis (MDA) is constructed. In this way, the coverage rate of the combination model is greatly improved, and the risk of miscarriage is effectively reduced. Lastly, the results of the combination model are analyzed by using the K-means clustering, and the clustering distribution is consistent with a normal distribution. The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
资助项目	[National Natural Science Foundation of China Key Project] ; [National Natural Science Foundation of China]
语种	英语
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/36420]
专题	系统科学研究所
作者单位	1.中央财经大学 2.天津大学 3.Zengcheng Branch of Guangdong Branch, Agricultural Bank of China Co. Ltd 4.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Li Jun,Pan Liang,Chen Muzi,et al. parametricandnonparametriccombinationmodeltoenhanceoverallperformanceondefaultprediction[J]. journalofsystemsscienceandcomplexity,2014,27(5):950.
APA	Li Jun,Pan Liang,Chen Muzi,&Yang Xiaoguang.(2014).parametricandnonparametriccombinationmodeltoenhanceoverallperformanceondefaultprediction.journalofsystemsscienceandcomplexity,27(5),950.
MLA	Li Jun,et al."parametricandnonparametriccombinationmodeltoenhanceoverallperformanceondefaultprediction".journalofsystemsscienceandcomplexity 27.5(2014):950.