Oil is widely perceived as a good diversification tool for stock markets. To fully understand the potential, we propose a new empirical methodology which combines generalized autoregressive score copula functions with high frequency data , and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence as well as quantiles of the distribution when compared to competing models. Using recently proposed conditional diversification benefits measure which take into account higher-order moments and nonlinear dependence  , we document reducing benefits from diversification over the past ten years. Diversification benefits implied by our empirical model are moreover strongly varying over time. These findings have important implications for portfolio management .
Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events , we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are , moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived .
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