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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 6546318, 7 pages
http://dx.doi.org/10.1155/2016/6546318
Research Article

Operational Risk Aggregation Based on Business Line Dependence: A Mutual Information Approach

1School of Economics and Business Administration, Beijing Normal University, Beijing 100875, China
2Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China

Received 14 January 2016; Accepted 31 March 2016

Academic Editor: Francisco R. Villatoro

Copyright © 2016 Wenzhou Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The dependencies between different business lines of banks have serious effects on the accuracy of operational risk estimation. Furthermore, the dependencies are far more complicated than simple linear correlation. While Pearson correlation coefficient is constructed based on the hypothesis of a linear association, the mutual information that measures all the information of a random variable contained in another random variable is a powerful alternative. Based on mutual information, the generalized correlation coefficient which can capture both linear and nonlinear correlation can be derived. This paper models the correlation between business lines by mutual information and normal copula. The experiment on a real-world Chinese bank operational risk data set shows that using mutual information to model the dependencies between business lines is more reasonable than linear correlation.