Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

Zhang, HG; Su, CW; Song, Y; Qiu, S; Xiao, R; Su, F

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

Zhang, HG Su, CW Song, Y Qiu, S Xiao, R Su, F

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Publication Type:: Journal Article
Citation:: Economic Modelling, 2017, 67 pp. 355 - 367
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Zhang, HG	en_US
dc.contributor.author	Su, CW	en_US
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Qiu, S	en_US
dc.contributor.author	Xiao, R	en_US
dc.contributor.author	Su, F https://orcid.org/0000-0002-9477-858X	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Economic Modelling, 2017, 67 pp. 355 - 367	en_US
dc.identifier.issn	0264-9993	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123908
dc.description.abstract	© 2017 Elsevier Ltd In this study, we propose a non-linear random mapping model called GELM. The proposed model is based on a combination of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Extreme Learning Machine (ELM), and can be used to calculate Value-at-Risk (VaR). Alternatively, the GELM model is a non-parametric GARCH-type model. Compared with conventional models, such as the GARCH models, ELM, and Support Vector Machine (SVM), the computational results confirm that the GELM model performs better in volatility forecasting and VaR calculation in terms of efficiency and accuracy. Thus, the GELM model can be an essential tool for risk management and stress testing.	en_US
dc.relation.ispartof	Economic Modelling	en_US
dc.relation.isbasedon	10.1016/j.econmod.2017.02.014	en_US
dc.subject.classification	Economics	en_US
dc.title	Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model	en_US
dc.type	Journal Article
utslib.citation.volume	67	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1502 Banking, Finance and Investment	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.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

© 2017 Elsevier Ltd In this study, we propose a non-linear random mapping model called GELM. The proposed model is based on a combination of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Extreme Learning Machine (ELM), and can be used to calculate Value-at-Risk (VaR). Alternatively, the GELM model is a non-parametric GARCH-type model. Compared with conventional models, such as the GARCH models, ELM, and Support Vector Machine (SVM), the computational results confirm that the GELM model performs better in volatility forecasting and VaR calculation in terms of efficiency and accuracy. Thus, the GELM model can be an essential tool for risk management and stress testing.

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http://hdl.handle.net/10453/123908