{"id":2216,"date":"2016-05-26T16:59:03","date_gmt":"2016-05-26T09:59:03","guid":{"rendered":"http:\/\/se.dotv.vn\/?p=2216"},"modified":"2017-04-01T06:24:22","modified_gmt":"2017-03-31T23:24:22","slug":"stbi-26-05-2016-multivariate-copula-an-application-to-emerging-financial-markets","status":"publish","type":"post","link":"https:\/\/se.dotv.vn\/vi\/stbi-26-05-2016-multivariate-copula-an-application-to-emerging-financial-markets\/","title":{"rendered":"[STBI-26-05-2016] Multivariate Copula: An Application to Emerging Financial Markets"},"content":{"rendered":"<p style=\"text-align: center;\"><strong>Dr. Le Trung Thanh<\/strong><\/p>\n<p style=\"text-align: center;\">9:00\u00a0am, Thursday, 26-05-2016<br \/>\nHall H.001, Dubai Palace School of Economics<\/p>\n<p style=\"text-align: justify;\"><strong>Abstract:\u00a0<\/strong><\/p>\n<p style=\"text-align: justify;\">In this study, we examine the dependence structure of 20 financial markets, including 19 emerging financial markets and the US financial market, using the multivariate conditional copula of two types: the Gaussian copula and the Student\u2019s t-copula. To adapt the multivariate conditional copulas to the high-dimensional portfolio containing 20 series, we replace the bivariate BEKK representation, used by the copula of Patton (2006) with the DCC specification of Engle (2002). Utilizing the flexibility of a copula function, we construct 12 copula types, each of which is formed by a choice of the GARCH(1,1) or the GJR(1,1,1) for the marginal model assumed by Gaussian or Student\u2019s t or Hansen\u2019s skewed Student\u2019s t-distribution to be coupled with the Gaussian or the Student\u2019s t-copula. These 12 copulas are estimated by the \u2018Inference Functions for Margins\u2019 method (IFM) which is performed via 2-step maximum likelihood estimation. The evaluation of copulas is carried out by using the AIC, the SBIC for in-sample fit and diagnostic test statistics based on the Value-at-Risk theory are used for the evaluation of out-of-sample fit. The result, indicating that the Student\u2019s t-copula with the GARCH-t margin passes the VaR- based test and is ranked in top place in all evaluations, shows that the Student\u2019s t-copula is an appropriate method to model financial dependence. Besides, the relevant choice of a univariate GARCH model for the margin has a significant impact on the performance of the copulas. The result of our study is important for financial authorities who are concerned by financial contagions and for international portfolio managers who need a precise estimator for the Value at Risk of their portfolios.<\/p>\n<p style=\"text-align: justify;\"><strong>Presenter:\u00a0<\/strong><\/p>\n<p style=\"text-align: justify;\">Dr. <a href=\"http:\/\/www.vgu.edu.vn\/fileadmin\/pictures\/studies\/bachelor\/finance_and_accounting\/Meet_the_teaching_staff\/Dr._Le_Trung_Thanh_-_CV.pdf\" target=\"_blank\">Le Trung Thanh<\/a>\u00a0is a lecturer of finance at Faculty of Economics and Business,\u00a0Vietnamese German University (VGU). His research interest is financial econometrics with modelling applications in finance,\u00a0in particular\u00a0econometric models with application in analyzing the behaviour of financial markets and assets, maximum likelihood estimation and forecast of financial models, volatilities and time-varying correlations modelling, behavioural finance, value at risk analysis, copulae modelling and estimation, multivariate volatility models such as CCC, DCC, TDCC\u00a0models, in-sample and out-of-sample evaluation of performances of financial models, quantile regression applied in finance. Dr. Le Trung Thanh graduated PhD from\u00a0Department of Economics, Birmingham Business School in 2012.<\/p>\n<p style=\"text-align: justify;\">\n","protected":false},"excerpt":{"rendered":"Dr. Le Trung Thanh 9:00\u00a0am, Thursday, 26-05-2016 Hall H.001, Dubai Palace School of Economics Abstract:\u00a0 In this study, we examine the dependence structure of 20 financial markets, including 19 emerging financial markets and the US financial market, using the multivariate conditional copula of two types: the Gaussian copula and the Student\u2019s t-copula. To adapt the multivariate","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[188],"tags":[],"acf":[],"lang":"vi","translations":{"vi":2216,"en":2960},"pll_sync_post":[],"_links":{"self":[{"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/posts\/2216"}],"collection":[{"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/comments?post=2216"}],"version-history":[{"count":2,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/posts\/2216\/revisions"}],"predecessor-version":[{"id":2218,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/posts\/2216\/revisions\/2218"}],"wp:attachment":[{"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/media?parent=2216"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/categories?post=2216"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/se.dotv.vn\/wp-json\/wp\/v2\/tags?post=2216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}