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

Zhang, HG; Wu, L; Song, Y; Su, CW; Wang, Q; Su, F

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

Zhang, HG Wu, L Song, Y Su, CW Wang, Q Su, F

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Publication Type:: Journal Article
Citation:: Cognitive Computation, 2018, 10 (2), pp. 187 - 200
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Zhang, HG	en_US
dc.contributor.author	Wu, L	en_US
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Su, CW	en_US
dc.contributor.author	Wang, Q	en_US
dc.contributor.author	Su, F	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	Cognitive Computation, 2018, 10 (2), pp. 187 - 200	en_US
dc.identifier.issn	1866-9956	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134313
dc.description.abstract	© 2017, Springer Science+Business Media, LLC. 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.	en_US
dc.relation.ispartof	Cognitive Computation	en_US
dc.relation.isbasedon	10.1007/s12559-017-9516-y	en_US
dc.title	An Online Sequential Learning Non-parametric Value-at-Risk Model for High-Dimensional Time Series	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1109 Neurosciences	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2017, Springer Science+Business Media, LLC. 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.

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http://hdl.handle.net/10453/134313