{"id":2508,"date":"2025-10-14T17:36:40","date_gmt":"2025-10-14T20:36:40","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=2508"},"modified":"2025-10-14T17:36:40","modified_gmt":"2025-10-14T20:36:40","slug":"ciclo-de-seminarios-2025-profa-dra-paula-tofoli","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-2025-profa-dra-paula-tofoli\/","title":{"rendered":"Ciclo de Semin\u00e1rios 2025 \u2013 Prof\u00aa. Dr\u00aa. Paula T\u00f3foli"},"content":{"rendered":"<p><strong>Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS).<\/strong><\/p>\n<p><strong>T\u00edtulo:<\/strong> Forecasting Volatility via Dynamic Copula-Based Regressions.<\/p>\n<p style=\"text-align: justify;\"><strong>Resumo:<\/strong> Realized volatility series are generally found to exhibit very slowly decaying autocorrelation functions, suggesting the use of long-memory models. The linear HAR regression model is well known for its ability to parsimoniously model the long-memory behavior of realized volatility. Besides, there is vast empirical evidence of its good forecasting performance. We propose an alternative approach to the linear HAR model, considering a dynamic vine copula based regression capable of capturing long-memory and nonlinearity in the time series dependence of realized volatility, and it also allows for the possibility of changes in the dependence structure over time. More specifically, we derive a regression from a dynamic D-vine decomposition of the joint density of the dependent and explanatory variables of the HAR model and compute point forecasts of volatility for the one-day horizon from this regression model. We evaluate the forecasting ability of the dynamic HAR-D-vine model in an empirical study. Our data sets consist of realized volatilities from the S&amp;P 500, NASDAQ 100, DOW JONES, FTSE 100, CAC and HANG SENG stock indexes. Regarding the main results, we find that both static and dynamic D-vine copula-based HAR regressions and the linear HAR model have a statistically equivalent forecast performance, while they outperform the bivariate\u00a0copula\u00a0model.<\/p>\n<p><strong>Data: 21\/10\/2025<\/strong><br \/>\n<strong>Hor\u00e1rio: 13:30<\/strong><\/p>\n<p>O semin\u00e1rio acontecer\u00e1 na<strong>\u00a0modalidade Online<\/strong>\u00a0e poder\u00e1 ser assistido pelo link: https:\/\/mconf.ufrgs.br\/webconf\/ppgest<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS). T\u00edtulo: Forecasting Volatility via Dynamic Copula-Based Regressions. Resumo: Realized volatility series are generally found to exhibit very slowly decaying autocorrelation functions, suggesting the use of long-memory models. The linear HAR regression model is well known &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-2025-profa-dra-paula-tofoli\/\"> <span class=\"screen-reader-text\">Ciclo de Semin\u00e1rios 2025 \u2013 Prof\u00aa. Dr\u00aa. Paula T\u00f3foli<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":2509,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"categories":[13],"tags":[],"_links":{"self":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2508"}],"collection":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/comments?post=2508"}],"version-history":[{"count":1,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2508\/revisions"}],"predecessor-version":[{"id":2510,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/posts\/2508\/revisions\/2510"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media\/2509"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=2508"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/categories?post=2508"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/tags?post=2508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}