{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T15:41:04Z","timestamp":1791819664051,"version":"4.3.4"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2001,11,1]],"date-time":"2001-11-01T00:00:00Z","timestamp":1004572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2001,11,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We propose a new generalized autoregressive conditional heteroscedastic (GARCH) model with tree-structured multiple thresholds for the estimation of volatility in financial time series. The approach relies on the idea of a binary tree where every terminal node parameterizes a (local) GARCH model for a partition cell of the predictor space. The fitting of such trees is constructed within the likelihood framework for non-Gaussian observations: it is very different from the well-known regression tree procedure which is based on residual sums of squares. Our strategy includes the classical GARCH model as a special case and allows us to increase model complexity in a systematic and flexible way. We derive a consistency result and conclude from simulation and real data analysis that the new method has better predictive potential than other approaches.<\/jats:p>","DOI":"10.1111\/1467-9868.00309","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T19:37:12Z","timestamp":1047497832000},"page":"727-744","source":"Crossref","is-referenced-by-count":42,"title":["Tree-structured Generalized Autoregressive Conditional Heteroscedastic Models"],"prefix":"10.1093","volume":"63","author":[{"given":"Francesco","family":"Audrino","sequence":"first","affiliation":[{"name":"Eidgen\u00f6ssiche Technische Hochschule Z\u00fcrich , Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"B\u00fchlmann","sequence":"additional","affiliation":[{"name":"Eidgen\u00f6ssiche Technische Hochschule Z\u00fcrich , Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2002,4,21]]},"container-title":["Journal of the Royal Statistical Society Series B: Statistical Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2F1467-9868.00309","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/63\/4\/727\/49590797\/jrsssb_63_4_727.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/63\/4\/727\/49590797\/jrsssb_63_4_727.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T11:33:03Z","timestamp":1679484783000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jrsssb\/article\/63\/4\/727\/7083408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,11,1]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2002,4,21]]},"published-print":{"date-parts":[[2001,11,1]]}},"URL":"https:\/\/doi.org\/10.1111\/1467-9868.00309","relation":{},"ISSN":["1369-7412","1467-9868"],"issn-type":[{"value":"1369-7412","type":"print"},{"value":"1467-9868","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2001,11]]},"published":{"date-parts":[[2001,11,1]]}}}