Hidden Coupling: Rethinking Commodity Diversification with Mutual Information

Hidden Coupling: Rethinking Commodity Diversification with Mutual Information | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hidden Coupling: Rethinking Commodity Diversification with Mutual Information Thiago Gil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8041591/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The case for commodity diversification is usually argued from linear correlation, yet allocation decisions can be questioned precisely when correlation misses nonlinear co-movement. This paper asks: do commodities truly diversify equities when it matters, and can we size exposure accordingly? Using daily ETFs from 2020–2025, I compare absolute Pearson correlation with mutual information (MI) and benchmark observed MI against its Gaussian-implied value at the prevailing correlation. Two results stand out. In calm or trending markets, both commodity-laden and non-commodity mixes explain very little of SPY’s uncertainty: correlation overstates the practical distinctiveness of commodities. In systemic stress (e.g., 2020), portfolios can exhibit low correlation yet sharply elevated MI, revealing hidden, nonlinear coupling consistent with financialization, even as commodity sleeves deliver right-tail payoffs. I then operationalize MI in a sizing overlay that caps the commodity sleeve when market-scaled MI exceeds its Gaussian benchmark. In out-of-sample tests (2020–2024), the MI rule delivers very small, directionally favorable reductions in expected shortfall and drawdown relative to an identically constrained correlation trigger, but with materially lower turnover. MI is thus a practical complement to correlation, useful for detecting regime shifts and sizing commodity exposure when dependence intensifies near the left tail. Financial Mathematics Commodities Correlation Portfolio diversification Mutual Information Asset allocation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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