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)


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.


Artificial Neural Network Value-at-Risk Risk management 


  1. 1.
    Rawls, S.W., Smithson, C.W.: Strategic risk management. J. Appl. Corp. Finan. 2(4), 6–18 (1990)CrossRefGoogle Scholar
  2. 2.
    Nadarajah, S., Chan, S.: Estimation methods for value at risk. In: Extreme Events in Finance, pp. 283–356 (2016)Google Scholar
  3. 3.
    Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Brooks, C.: Introductory Econometrics for Finance. Cambridge University Press, Cambridge (2017)zbMATHGoogle Scholar
  5. 5.
    Locarek-Junge, H., Prinzler, R.: Estimating value-at-risk using neural networks. In: Informationssysteme in der Finanzwirtschaft, pp. 385–397 (1998)Google Scholar
  6. 6.
    Chen, X., Lai, K.K., Yen, J.: A statistical neural network approach for value-at-risk analysis. In: International Joint Conference on Computational Sciences and Optimization, vol. 2, pp. 17–21. IEEE (2009)Google Scholar
  7. 7.
    Iii, J.P.: Statistical inference using extreme order statistics. Ann. Stat. 3(1), 119–131 (1975)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hosking, J.R.M., Wallis, J.R.: Parameter and quantile estimation for the generalized pareto distribution. Technometrics 29(3), 339 (1987)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O.: Neural Network Design, vol. 20 (1996)Google Scholar
  10. 10.
    White, H.: Nonparametric estimation of conditional quantiles using neural networks. In: Computing Science and Statistics, pp. 190–199 (1992)Google Scholar
  11. 11.
    Ehm, W., Gneiting, T., Jordan, A., Krüger, F.: Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 78(3), 505–562 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Nelson, D.B.: Conditional Heteroskedasticity in asset returns: a new approach. Econometrica 59(2), 347 (1991)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kennon, J.: What Are the Sectors and Industries of the S&P 500? The Balance. Accessed 1 Nov 2018

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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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