{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:23:36Z","timestamp":1791667416975,"version":"4.3.4"},"reference-count":50,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T00:00:00Z","timestamp":1533772800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2018,11]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We develop a dynamic model for the intraday dependence between discrete stock price changes. The conditional copula mass function for the integer tick\u2010size price changes has time\u2010varying parameters that are driven by the score of the predictive likelihood function. The marginal distributions are Skellam and also have score\u2010driven time\u2010varying parameters. We show that the integration steps in the copula mass function for large dimensions can be accurately approximated via numerical integration. The resulting computational gains lead to a methodology that can treat high\u2010dimensional applications. Its accuracy is shown by an extensive simulation study. In our empirical application of 10 US bank stocks, we reveal strong evidence of time\u2010varying intraday dependence patterns: Dependence starts at a low level but generally rises during the day. Based on one\u2010step\u2010ahead out\u2010of\u2010sample density forecasting, we find that our new model outperforms benchmarks for intraday dependence such as the cubic spline model, the fixed correlation model, or the rolling average realized correlation.<\/jats:p>","DOI":"10.1002\/jae.2645","type":"journal-article","created":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T04:28:42Z","timestamp":1533788922000},"page":"966-985","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Dynamic discrete copula models for high\u2010frequency stock price changes"],"prefix":"10.1002","volume":"33","author":[{"given":"Siem Jan","family":"Koopman","sequence":"first","affiliation":[{"name":"Vrije Universiteit Amsterdam and Tinbergen Institute  The Netherlands"},{"name":"CREATES Aarhus University  Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rutger","family":"Lit","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Amsterdam and Tinbergen Institute  The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Lucas","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Amsterdam and Tinbergen Institute  The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Opschoor","sequence":"additional","affiliation":[{"name":"Vrije Universiteit Amsterdam and Tinbergen Institute  The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,8,9]]},"reference":[{"key":"e_1_2_7_2_1","volume-title":"Handbook of mathematical functions","author":"Abramowitz M.","year":"1972"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/1.1.3"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/13\/2\/025010"},{"issue":"2","key":"e_1_2_7_5_1","first-page":"465","article-title":"Poisson difference integer valued autoregressive model of order one","volume":"37","author":"Alzaid A. 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