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Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors

  • Siu CheungEmail author
  • Ziqi Chen
  • Yanli Li
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)

Abstract

Previous studies on estimating Value-at-Risk mostly focus on the market index or specific portfolio, while few has been done on specific business sectors. In this paper, we compare the Value-at-Risk estimations from different methods, namely Artificial Neural Network model, extreme value theory-based method, and Monte Carlo simulation. We show that while non-parametric approaches such as Monte Carlo simulation performs better marginally, Artificial Neural Network has great potential for future development.

Keywords

Artificial Neural Network Value-at-Risk Risk management 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Statistics and Actuarial Science, Faculty of MathematicsUniversity of WaterlooWaterlooCanada
  2. 2.Department of Statistical and Actuarial Science, Faculty of ScienceWestern UniversityLondonCanada

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