A Neural Network Model on Solving Multiobjective Conditional Value-at-Risk

Conditional Value-at-Risk (CVaR) is a new approach for credit risk optimization in the field of finance engineering. This paper introduces the concept of α-CVaR for the case of multiple losses under the confidence level vector α. The problem of solving the minimal α-CVaR results in a multiobjective problem (MCVaR). In order to get Pareto efficient solutions of the (MCVaR), we introduce a single objective problem (SCVaR) and show that the optimal solutions of the (SCVaR) are Pareto efficient solutions of (MCVaR). We construct a nonlinear neural networks model with an approximate problem (SCVaR)′ of (SCVaR). We may get an approximate solution (SCVaR) by solving this nonlinear neural networks model.

Authors and Affiliations

1. School of Economics and Management, Xidian University, Xi’an, 710071, China

   Min Jiang

2. College of Business and Administration, Zhejiang University of Technology, Hangzhou, 310032, China

   Zhiqing Meng

3. College of International Business & Management, Shanghai University, Shanghai, 201800, China

   Qiying Hu

Editors and Affiliations

1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

   Fu-Liang Yin

2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

   Chengan Guo

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