Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/76434
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dc.contributor.authorChukiat Chaiboonsrien_US
dc.contributor.authorSatawat Wannapanen_US
dc.date.accessioned2022-10-16T07:09:59Z-
dc.date.available2022-10-16T07:09:59Z-
dc.date.issued2021-03-01en_US
dc.identifier.issn22277099en_US
dc.identifier.other2-s2.0-85106472707en_US
dc.identifier.other10.3390/economies9010013en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85106472707&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/76434-
dc.description.abstractThe advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the non-novel quantum mechanism, it does interestingly connect with the distributional signals of humans’ brainstorms. The highlighted purpose of this article is to devise a quantum-wave distribution methodically to analyze better risks and returns for stock markets in The Association of Southeast Asian Nations (ASEAN) countries, including Thailand (SET), Singapore (STI), Malaysia (FTSE), Philippines (PSEI), and Indonesia (PCI). Data samples were observed as quarterly trends between 1994 and 2019. Bayesian statistics and simulations were applied to present estimations’ outputs. Empirically, quantum distributions are remarkable for providing “real distributions”, which computationally conform to Bayesian inferences and crucially contribute to the higher level of extreme data analyses in financial economics.en_US
dc.subjectEconomics, Econometrics and Financeen_US
dc.subjectSocial Sciencesen_US
dc.titleApplying quantum mechanics for extreme value prediction of VaR and ES in the ASEAN stock exchangeen_US
dc.typeJournalen_US
article.title.sourcetitleEconomiesen_US
article.volume9en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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