Dynamic financial contagion prediction model based on fuzzy information granularity SVM

Author

Lin Liu ; Yingfeng Shao ; Xiaofeng Hui

Author_Institution

Sch. of Manage., Harbin Inst. of Technol., Harbin, China

fYear

2013

fDate

19-21 Nov. 2013

Firstpage

545

Lastpage

550

Abstract

Contagion time prediction is an important research topic in financial crises. This article put forward a prediction model of contagion time based on fuzzy information granularity SVM. It uses granularity fuzzy and SVM to estimate the bounds of stock index, and further forecast the similarity index. The predicted contagion time from the United States to the United Kingdom, Germany, Frence and China are tested, and compared with the real ones. The empirical analyses comfirm that the model is a feasible method to predict the financial contagion arrival time.

Keywords

financial management; fuzzy set theory; stock markets; support vector machines; contagion time prediction; dynamic financial contagion prediction model; financial contagion arrival time; financial crises; fuzzy information granularity SVM; research topic; similarity index; stock index; Forecasting; Indexes; Prediction algorithms; Predictive models; Support vector machines; Time series analysis; Training; contagion arrival time; financial crisis; fuzzy information granularit; nonlinear similarity; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Informatics (CINTI), 2013 IEEE 14th International Symposium on

Conference_Location

Budapest

Print_ISBN

978-1-4799-0194-4

Type

conf

DOI

10.1109/CINTI.2013.6705257

Filename

6705257