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The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns of three major stock indexes yields strong empirical support for a temporal clustering of both the occurrence and the size of extremes. Backtesting value\u2010at\u2010risk and expected shortfall forecasts shows that the consideration of clustering effects and of feedback between the magnitudes and the intensity of extremes results in better forecasts of\u00a0risk.<\/jats:p>","DOI":"10.1002\/jae.2741","type":"journal-article","created":{"date-parts":[[2019,9,9]],"date-time":"2019-09-09T02:14:40Z","timestamp":1567995280000},"page":"248-272","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Multivariate dynamic intensity peaks\u2010over\u2010threshold models"],"prefix":"10.1002","volume":"35","author":[{"given":"Nikolaus","family":"Hautsch","sequence":"first","affiliation":[{"name":"Department of Statistics and Operations Research University of Vienna, Research Platform \u201cData Science @ Uni Vienna\u201d, Vienna Graduate School of Finance Vienna Austria"},{"name":"Center for Financial Studies Frankfurt Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo","family":"Herrera","sequence":"additional","affiliation":[{"name":"Facultad de Econom\u00eda y Negocios Universidad de Talca Talca Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,12,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2015.03.002"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2014.05.006"},{"key":"e_1_2_10_4_1","unstructured":"BCBS(2013).Fundamental review of the trading book: A revised market risk framework. 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