{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:10:24Z","timestamp":1785420624005,"version":"3.56.0"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Despite its conceptual appeal, the autoregressive inverse\u2010Wishart (AIW) multivariate stochastic volatility model has been hindered by inefficient sampling methods. The existing samplers for the latent covariance matrix severely suffer from the curse of dimensionality and only work when the dimension is very low. In this paper, we introduce a new proposal for the latent covariance matrix in the AIW model, which demonstrates far better scalability. We introduce different extensions to the basic AIW model that capture various stylized volatility features. We further fit the AIW model into a factor structure and provide an a posteriori identification procedure without introducing order dependence. When evaluated using real datasets ranging from 10 assets to 1000 assets, the new AIW\u2010based models, whether the standalone versions or the factor versions, are especially suited for multivariate volatility modeling in a wide range of dimensions.<\/jats:p>","DOI":"10.1002\/jae.70084","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:38:42Z","timestamp":1785418722000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Autoregressive Inverse\u2010Wishart Multivariate Stochastic Volatility Model and Its Factor Extension"],"prefix":"10.1002","author":[{"given":"Xin","family":"Jin","sequence":"first","affiliation":[{"name":"School of Economics Shanghai University of Finance and Economics  Shanghai China"},{"name":"Key Laboratory of Mathematical Economics (SUFE), Ministry of Education  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"issue":"2","key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.jeconom.2022.04.013","article-title":"Macroeconomic Forecasting and Variable Ordering in Multivariate Stochastic Volatility Models","volume":"235","author":"Arias J. 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Working Paper 42247 Munich Personal RePEc Archive.https:\/\/mpra.ub.uni\u2010muenchen.de\/56804\/1\/MPRA_paper_56804.pdf."},{"issue":"1","key":"e_1_2_9_13_1","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jeconom.2018.11.007","article-title":"Sparse Bayesian Time\u2010Varying Covariance Estimation in Many Dimensions","volume":"210","author":"Kastner G.","year":"2019","journal-title":"Journal of Econometrics"},{"issue":"4","key":"e_1_2_9_14_1","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/10618600.2017.1322091","article-title":"Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models","volume":"26","author":"Kastner G.","year":"2017","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"2","key":"e_1_2_9_15_1","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1214\/12-AOS989","article-title":"Nonlinear Shrinkage Estimation of Large\u2010Dimensional Covariance Matrices","volume":"40","author":"Ledoit O.","year":"2012","journal-title":"Annals of Statistics"},{"issue":"4","key":"e_1_2_9_16_1","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1111\/jtsa.12795","article-title":"Exact Likelihood for Inverse Gamma Stochastic Volatility Models","volume":"46","author":"Leon\u2010Gonzalez R.","year":"2025","journal-title":"Journal of Time Series Analysis"},{"issue":"1","key":"e_1_2_9_17_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/07350015.2020.1806853","article-title":"Leverage, Asymmetry, and Heavy Tails in the High\u2010Dimensional Factor Stochastic Volatility Model","volume":"40","author":"Li M.","year":"2022","journal-title":"Journal of Business & Economic Statistics"},{"issue":"6","key":"e_1_2_9_18_1","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1002\/jae.1260","article-title":"Multivariate High\u2010Frequency\u2010Based Volatility (HEAVY) Models","volume":"27","author":"Noureldin D.","year":"2012","journal-title":"Journal of Applied Econometrics"},{"issue":"1","key":"e_1_2_9_19_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jeconom.2013.10.003","article-title":"Multivariate Rotated ARCH Models","volume":"179","author":"Noureldin D.","year":"2014","journal-title":"Journal of Econometrics"},{"issue":"4","key":"e_1_2_9_20_1","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1080\/07350015.2016.1245622","article-title":"New HEAVY Models for Fat\u2010Tailed Realized Covariances and Returns","volume":"36","author":"Opschoor A.","year":"2018","journal-title":"Journal of Business & Economic Statistics"},{"issue":"3","key":"e_1_2_9_21_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1198\/073500105000000306","article-title":"Multivariate Stochastic Volatility via Wishart Processes","volume":"24","author":"Philipov A.","year":"2006","journal-title":"Journal of Business & Economic Statistics"},{"key":"e_1_2_9_22_1","volume-title":"Applied Multivariate Analysis: Using Bayesian and Frequentist Methods of Inference","author":"Press S. 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