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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/662
DC FieldValueLanguage
dc.contributor.authorTran Vu Longen_US
dc.date.accessioned2020-03-12T17:06:32Z-
dc.date.available2020-03-12T17:06:32Z-
dc.date.issued2016-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/662-
dc.description.abstractIt has long been common to use value-at-risk (VaR) as a tool for portfolio risk management. However, despite having many existing multivariate models, VaR is still mostly modeled and forecasted single-dimensionally, and multidimensional applications of VaR are not really that popular for modelers. Therefore, this thesis aim to use multivariate model is applied to estimate and forecast VaR. A data set of 9 national stock indices from the US, the UK, Germany, Japan, South Korea, China, Hongkong, Singapore and Vietnam are collected. To estimate VaR, a vector autoregressive model is combined with a time-varying conditional correlation multivariate GARCH model. The forecasting method is one-day ahead out-of-sample VaR at two different significance level under normal distribution. The results are evaluated by studying the violation rate. The results show that among 9 indices, the forecasted VaRs have higher performance at 99% significance level. The forecasted VaR of VNI come out on top with the lowest violation rate, while VaR of N225 have the highest number of failure cases. It is also observed that VaR underperforms in extreme events.en_US
dc.language.isoenen_US
dc.publisherVietnamese-German Universityen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectMultivariate modelingen_US
dc.subjectValue at risken_US
dc.subjectVaRen_US
dc.titleMultivariare modeling and forecasting value at risken_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
Appears in Collections:Finance & Accounting (FA)
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