Cognitive Computation

, Volume 10, Issue 2, pp 187–200 | Cite as

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

  • Heng-Guo Zhang
  • Libo Wu
  • Yan Song
  • Chi-Wei Su
  • Qingping Wang
  • Fei Su
Article

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.

Keywords

OS-ELM GARCH models Value-at-Risk High-dimensional space Time series 

Notes

Compliance with Ethical Standards

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.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Heng-Guo Zhang
    • 1
  • Libo Wu
    • 2
  • Yan Song
    • 3
  • Chi-Wei Su
    • 4
  • Qingping Wang
    • 5
  • Fei Su
    • 6
  1. 1.School of Data ScienceFudan UniversityShanghaiChina
  2. 2.School of Economics & School of Data ScienceFudan UniversityShanghaiChina
  3. 3.College of Information Science and EngineeringOcean University of ChinaQingdaoChina
  4. 4.Department of FinanceOcean University of ChinaQingdaoChina
  5. 5.School of Mathematical SciencesOcean University of ChinaQingdaoChina
  6. 6.Finance Discipline Group, UTS Business SchoolUniversity of TechnologySydneyAustralia

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