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Oil is perceived as a good diversification tool for stock markets. To fullyunderstand this potential, we propose a new empirical methodology that combinesgeneralized autoregressive score copula functions with high frequency data andallows us to capture and forecast the conditional time-varying jointdistribution of the oil -- stocks pair accurately. Our realized GARCH withtime-varying copula yields statistically better forecasts of the dependence andquantiles of the distribution relative to competing models. Employing arecently proposed conditional diversification benefits measure that considershigher-order moments and nonlinear dependence from tail events, we documentdecreasing benefits from diversification over the past ten years. Thediversification benefits implied by our empirical model are, moreover, stronglyvaried over time. These findings have important implications for assetallocation, as the benefits of including oil in stock portfolios may not be aslarge as perceived.

Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data