We investigate correlations among six primary asset classes from January 1982 to December 2022. Our analysis extends existing literature, on the well-researched stock-bond correlation (SBC), by encompassing 14 supplementary asset class dyads and four correlational regimes. We challenge the archetype of correlational time-invariance that underlies buy-and-hold asset allocation strategies by implementing structural break tests and an innovative Wavelet Coherence (WC) methodology, where our findings reveal temporal instability. Through a multi-method statistical approach, we present robust and persuasive evidence of macroeconomic factors as determinants of temporal change. Leveraging time-varying Granger causality, we unearth elusive yet significant relationships. Our research pivots to practice, illustrating outperformance in portfolios constructed upon the principles of time-varying change, macro drivers, and correlational regimes, thus enabling investors to make more informed decisions, leading to superior risk-adjusted returns amid a dynamically evolving economic landscape.
","abstract_html":"<p>We investigate correlations among six primary asset classes from January 1982 to December 2022. Our analysis extends existing literature, on the well-researched stock-bond correlation (SBC), by encompassing 14 supplementary asset class dyads and four correlational regimes. We challenge the archetype of correlational time-invariance that underlies buy-and-hold asset allocation strategies by implementing structural break tests and an innovative Wavelet Coherence (WC) methodology, where our findings reveal temporal instability. Through a multi-method statistical approach, we present robust and persuasive evidence of macroeconomic factors as determinants of temporal change. Leveraging time-varying Granger causality, we unearth elusive yet significant relationships. Our research pivots to practice, illustrating outperformance in portfolios constructed upon the principles of time-varying change, macro drivers, and correlational regimes, thus enabling investors to make more informed decisions, leading to superior risk-adjusted returns amid a dynamically evolving economic landscape.</p>","abstract_has_math":false,"creators":["Williams, T.H."],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Jack Strauss","Alex Petkevich","Chris Hughen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T02:02:45Z","subjects":["Portfolio optimization","Stock-bond correlation","Tactical asset allocation","Time-varying Granger-causality","Wavelet coherence","Business","Corporate Finance","Finance and Financial Management"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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