{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T03:45:22Z","timestamp":1791949522158,"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\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Dynamic Conditional Correlation (DCC) GARCH models are the workhorse of multivariate volatility forecasting, yet it is an open empirical question whether statistical gains in covariance forecasting translate into better portfolio outcomes. We study this question in a deliberately controlled three-asset universe (S&amp;amp;P 500, NASDAQ Composite, WTI crude oil) using 5,011 daily log returns from January 1999 to December 2018 and a strictly out-of-sample design: 3,511 daily forecasts (10 January 2005 to 28 December 2018) from a rolling 1,500-day window re-estimated every 63 trading days. Marginal dynamics follow GJR-GARCH(1,1) with Student-t innovations and correlations follow DCC(1,1) estimated by two-step quasi maximum likelihood (mean estimates a = 0.037, b = 0.925). Evaluated against the realized outer-product proxy, DCC one-step-ahead covariance forecasts reduce mean Frobenius norm loss by 8.7% relative to a trailing 250-day sample covariance (Newey\u2013West t = 4.58, p &amp;lt;0.001). Conditional S&amp;amp;P 500\u2013WTI correlation ranges from \u22120.50 to +0.72, but its mean is lower in the September 2008\u2013June 2009 crisis window (0.20) than in calm periods (0.24). In a monthly-rebalanced, long-only minimum-variance backtest net of 10 bps proportional costs, the DCC portfolio attains 18.48% annualized volatility versus 20.27% for equal weight (\u22128.8%), but does not improve on 500-day sample-covariance (18.34%) or Ledoit\u2013Wolf (18.33%) minimum-variance portfolios, and has the lowest return-to-risk ratio of the four strategies (0.22 versus 0.29\u20130.31). We conclude that, in small-N universes with a binding long-only constraint, a statistically significant covariance-forecast improvement need not be economically exploitable through minimum-variance optimization, and we outline the mechanisms and the tests required to establish this more firmly.<\/jats:p>","DOI":"10.2139\/ssrn.7598879","type":"posted-content","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:55:22Z","timestamp":1791946522000},"source":"Crossref","is-referenced-by-count":0,"title":["Does Dynamic Conditional Correlation Improve Minimum-Variance Allocation? Out-of-Sample Evidence from a Three-Asset Equity-Commodity Universe"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6832-8957","authenticated-orcid":true,"given":"Arin","family":"Gopalan Yadav","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:55:22Z","timestamp":1791946522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7598879"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7598879","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}