Skip to main content

Advertisement

SpringerLink
Log in

An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Online Value-at-Risk (VaR) analysis in high-dimensional space remains a challenge in the era of big data. In this paper, we propose an online sequential learning non-parametric VaR model called OS-GELM which is an autonomous cognitive system. This model uses a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process and an online sequential extreme learning machine (OS-ELM) to cognitively calculate VaR, which can be used for online risk analysis. The proposed model not only learns the data one-by-one or chunk-by-chunk but also calculates VaR in real time by extending OS-ELM from machine learning to the non-parametric GARCH process. The GARCH process is also extended to one-by-one and chunk-by-chunk mode. In OS-GELM, the parameters of hidden nodes are randomly selected. The output weights are analytically determined based on the sequentially arriving data. In addition, the generalization performance of the OS-GELM model attains a small training error and generates the smallest norm of weights. Experimentally obtained VaRs are compared with those given by GARCH-type models and conventional OS-ELM. The computational results demonstrate that the OS-GELM model obtains more accurate results and is better at forecasting the online VaR. OS-GELM model is an autonomous cognitive system to dynamically calculate Value-at-Risk, which can be used for online financial risk assessment about human being’s behavior. The OS-GELM model can calculate VaR in real time, which can be used as a tool for online risk management. OS-GELM can handle any bounded, non-constant, piecewise-continuous membership function to realize real-time VaR monitoring.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Youssef M, Belkacem L, Mokni K. Value-at-risk estimation of energy commodities: a long-memory GARCH–EVT approach. Energy Econ. 2015;51:99–110.

    Article  Google Scholar 

  2. Kim M, Lee S. Nonlinear expectile regression with application to value-at-risk and expected shortfall estimation. Comput Stat Data Anal. 2016;94:1–19.

    Article  Google Scholar 

  3. Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. Neural Netw IEEE Trans. 2006;17(6):1411–23.

    Article  Google Scholar 

  4. Platt J. A resource-allocating network for function interpolation. Neural Comput. 1991;3(2):213–25.

    Article  Google Scholar 

  5. Bildirici M, Ersin ÖÖ. Improving forecasts of garch family models with the artificial neural networks: an application to the daily returns in istanbul stock exchange. Expert Syst Appl. 2009;36(4):7355–62.

    Article  Google Scholar 

  6. Ghorbel A, Trabelsi A. Energy portfolio risk management using time-varying extreme value copula methods. Econ Model. 2014;38:470–85.

    Article  Google Scholar 

  7. Berman JJ, Principles of big data: preparing, sharing, and analyzing complex information, Newnes, 2013.

  8. Li G, Liu M, Dong M. A new online learning algorithm for structure adjustable extreme learning machine. Comput Math Appl. 2010;60(3):377–89.

    Article  Google Scholar 

  9. Grossberg S. Nonlinear neural networks: principles, mechanisms, and architectures. Neural Netw. 1988;1(1):17–61.

    Article  Google Scholar 

  10. LeCun YA, Bottou L, Orr GB, Müller K-R, Efficient backprop, in: Neural Networks: Tricks of the Trade, Springer, 2012:9–48.

  11. Huang G-B, Saratchandran P, Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. Syst Man Cybern Part B: Cybern IEEE Trans. 2004;34(6):2284–92.

    Article  Google Scholar 

  12. Zou H, Jiang H, Lu X, Xie L, An online sequential extreme learning machine approach to WiFi based indoor positioning, in: Internet of Things (WF-IoT), 2014 I.E. World Forum on, IEEE, 2014:111–116.

  13. Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theoryand applications. Neurocomputing. 2006;70(1):489–501.

    Article  Google Scholar 

  14. Golub GH, Van Loan CF, Matrix computations, 3rd (2012).

  15. Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. Syst Man Cybern Part B: Cybern IEEE Trans. 2012;42(2):513–29.

    Article  Google Scholar 

  16. Tamura S, Tateishi M. Capabilities of a four-layered feedforward neuralnetwork: four layers versus three. Neural Netw IEEE Trans. 1997;8(2):251–5.

    Article  CAS  Google Scholar 

  17. Chong EK, Zak SH, An introduction to optimization, Vol. 76, John Wiley & Sons, 2013.

  18. Giot P, Laurent S. Modelling daily value-at-risk using realized volatility and arch type models. J Empir Financ. 2004;11(3):379–98.

    Article  Google Scholar 

  19. Kupiec P. Techniques for verifiying the accuracy of risk management models. J Deriv. 1995;3:73–84.

    Article  Google Scholar 

  20. Engle RF, Manganelli S. CAViaR: conditional autoregressive value at risk by regression quantiles. J Bus Econ Stat. 2004;22(4):367–81.

    Article  Google Scholar 

  21. Engle RF, Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation, Econometrica: Journal of the Econometric Society 1982: 987–1007.

  22. He K, Lai KK, Yen J. Ensemble forecasting of value at risk via multi resolution analysis based methodology in metals markets. Expert Syst Appl. 2012;39(4):4258–67.

    Article  Google Scholar 

  23. Vong C-M, et al. Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine. Cogn Comput. 2015;7(3):381–91.

    Article  Google Scholar 

  24. Savitha R, Suresh S, Kim HJ. A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput. 2014;6(2):253–63.

    Article  Google Scholar 

  25. Cao K, et al. Classification of uncertain data streams based on extreme learning machine. Cogn Comput. 2015;7(1):150–60.

    Article  Google Scholar 

Download references

Funding

This research is supported by China Postdoctoral Science Foundation funded project, the National Social Science Foundation (15BJY155) and the National High Technology Research and Development Program of China (863 Program) (2015AA050203).

Author information

Authors and Affiliations

  1. School of Data Science, Fudan University, Shanghai, China

    Heng-Guo Zhang

  2. School of Economics & School of Data Science, Fudan University, Shanghai, China

    Libo Wu

  3. College of Information Science and Engineering, Ocean University of China, Qingdao, China

    Yan Song

  4. Department of Finance, Ocean University of China, Qingdao, China

    Chi-Wei Su

  5. School of Mathematical Sciences, Ocean University of China, Qingdao, Shandong, China

    Qingping Wang

  6. Finance Discipline Group, UTS Business School, University of Technology, Sydney, Australia

    Fei Su

Authors
  1. Heng-Guo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Libo Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chi-Wei Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fei Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heng-Guo Zhang.

Ethics declarations

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, HG., Wu, L., Song, Y. et al. An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series. Cogn Comput 10, 187–200 (2018). https://doi.org/10.1007/s12559-017-9516-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-017-9516-y

Keywords

Search

Navigation