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Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of the marginal distributions are estimated, and then those of the copula. Parameters of the latent processes (volatilities and dependence) are estimated using efficient importance sampling. We discuss goodness\u2010of\u2010fit tests and ways to forecast the dependence parameter. For two bivariate stock index series, we show that the proposed model outperforms standard competing models. Copyright \u00a9 2010 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.1197","type":"journal-article","created":{"date-parts":[[2010,7,2]],"date-time":"2010-07-02T09:14:48Z","timestamp":1278062088000},"page":"269-295","source":"Crossref","is-referenced-by-count":154,"title":["Dynamic stochastic copula models: estimation, inference and applications"],"prefix":"10.1002","volume":"27","author":[{"given":"Christian M.","family":"Hafner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans","family":"Manner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2012,2,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.insmatheco.2007.02.001"},{"key":"e_1_2_8_3_1","unstructured":"AmisanoG CasarinR.2007.Particle filters for Markov switching stochastic correlation models. 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