Skip to main content
SpringerLink
Log in
Book cover

INNS Big Data and Deep Learning conference

INNSBDDL 2019: Recent Advances in Big Data and Deep Learning pp 267–275Cite as

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

  • Conference paper
  • First Online:

Part of the book series: Proceedings of the International Neural Networks Society ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Rawls, S.W., Smithson, C.W.: Strategic risk management. J. Appl. Corp. Finan. 2(4), 6–18 (1990)

    Article  Google Scholar 

  2. Nadarajah, S., Chan, S.: Estimation methods for value at risk. In: Extreme Events in Finance, pp. 283–356 (2016)

    Google Scholar 

  3. Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econ. 31(3), 307–327 (1986)

    Article  MathSciNet  Google Scholar 

  4. Brooks, C.: Introductory Econometrics for Finance. Cambridge University Press, Cambridge (2017)

    MATH  Google Scholar 

  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. 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. Iii, J.P.: Statistical inference using extreme order statistics. Ann. Stat. 3(1), 119–131 (1975)

    Article  MathSciNet  Google Scholar 

  8. Hosking, J.R.M., Wallis, J.R.: Parameter and quantile estimation for the generalized pareto distribution. Technometrics 29(3), 339 (1987)

    Article  MathSciNet  Google Scholar 

  9. Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O.: Neural Network Design, vol. 20 (1996)

    Google Scholar 

  10. White, H.: Nonparametric estimation of conditional quantiles using neural networks. In: Computing Science and Statistics, pp. 190–199 (1992)

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  12. Nelson, D.B.: Conditional Heteroskedasticity in asset returns: a new approach. Econometrica 59(2), 347 (1991)

    Article  MathSciNet  Google Scholar 

  13. Kennon, J.: What Are the Sectors and Industries of the S&P 500? The Balance. https://www.thebalance.com/what-are-the-sectors-and-industries-of-the-sandp-500-3957507. Accessed 1 Nov 2018

Download references

Author information

Authors and Affiliations

  1. Department of Statistics and Actuarial Science, Faculty of Mathematics, University of Waterloo, Waterloo, Canada

    Siu Cheung & Yanli Li

  2. Department of Statistical and Actuarial Science, Faculty of Science, Western University, London, Canada

    Ziqi Chen

Authors
  1. Siu Cheung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siu Cheung .

Editor information

Editors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Luca Oneto

  2. Department of Mathematics, University of Padova, Padua, Italy

    Nicolò Navarin

  3. Department of Mathematics, University of Padova, Padua, Italy

    Alessandro Sperduti

  4. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Davide Anguita

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheung, S., Chen, Z., Li, Y. (2020). Comparing the Estimations of Value-at-Risk Using Artificial Network and Other Methods for Business Sectors. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_28

Download citation

Publish with us

Policies and ethics

Search

Navigation