{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T02:49:00Z","timestamp":1791514140738,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We propose constructing \"bespoke\" realized correlation matrices that are optimized to improve forecast performance when included in a parametric forecasting model. This is in contrast with standard realized measures which do not exploit information about the downstream use of the measure. In addition to extending the univariate framework of Patton and Zhang (2026) to the multivariate setting, we consider the use of stochastic state variables, such as volume and volatility, when determining the bespoke weights for high-frequency returns, and find both variables to carry useful information. Using high-frequency data for approximately 360 S&amp;amp;P 500 constituents over 2000-23, we demonstrate that bespoke correlation models significantly outperform benchmark models that do not tailor the realized measure.<\/jats:p>","DOI":"10.2139\/ssrn.7572359","type":"posted-content","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T01:50:43Z","timestamp":1791510643000},"source":"Crossref","is-referenced-by-count":0,"title":["Bespoke Realized Correlations for Multivariate Volatility Prediction"],"prefix":"10.2139","author":[{"given":"Berna","family":"Kotehne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6985-6216","authenticated-orcid":true,"given":"Andrew J.","family":"Patton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T01:50:43Z","timestamp":1791510643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7572359"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7572359","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}