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Nicholas Appiah, Ali Jaffri, Dilmi C. W. Hettiachchi-Halpe-Kankanamalage, Svetlozar T. Rachev · 2026-06-25
The paper tests different ways to build portfolios from 30 U.S. commodity ETFs (agriculture, energy, metals, broad index) using daily data from late 2018 to late 2024. It compares simple buy-and-hold against rolling optimized portfolios using mean-variance and CVaR (a downside-risk measure), in both long-only and long-short forms, and adds a dynamic model (ARMA-GARCH with a Student-t copula) for forecasting. It finds that conservative, downside-risk-aware methods (minimum-risk and CVaR) gave more stable performance and better risk-adjusted ratios, but none eliminated the risk of extreme losses.
Why it matters: Practitioners allocating across commodity ETFs may find that emphasizing downside-risk objectives like CVaR and minimum-variance produces steadier results than chasing return-maximizing tangent portfolios, which are fragile due to expected-return estimation error. The study also stresses that turnover and transaction costs materially affect whether dynamic optimization is worthwhile, favoring low-turnover approaches.
⚠ Results are backtested on a single six-year window of 30 commodity ETFs and may not generalize to other periods, assets, or live trading conditions.
This paper examines portfolio optimization for commodity exchange-traded funds (ETFs) under heavy-tailed return behavior. Using daily Bloomberg data for 30 U.S.-listed commodity ETFs from 12 December 2018 to 16 December 2024, we study funds spanning agriculture, energy, metals, and broad commodity index exposure. We compare a passive buy-and-hold portfolio with rolling-window optimized portfolios formed under mean--variance and conditional value-at-risk (CVaR) criteria, considering both long-only and restricted long--short strategies. The results showed substantial heterogeneity across commodity sectors, with energy and broad commodity index funds displaying pronounced volatility, skewness, and excess kurtosis. Historical optimization indicated that minimum-risk and CVaR-based portfolios provided more stable cumulative performance than tangent portfolios and generally improved Sharpe, Calmar, and STARR$_{0.95}$ ratios. Extreme-value diagnostics showed that optimized portfolios remained exposed to heavy downside tails, so improved risk-adjusted performance did not eliminate extreme-loss risk. A dynamic extension based on ARMA--GARCH marginal models, Student--$t$ copula dependence, and one-step-ahead predictive scenarios improved performance mainly when combined with minimum-risk or CVaR-based objectives. Dynamic mean--variance tangent portfolios performed less reliably, reflecting sensitivity to expected-return estimation error. Transaction-cost robustness checks further showed that the practical value of dynamic optimization depended on turnover control, with low-turnover dynamic CVaR tangent portfolios remaining more resilient to implementation costs. Overall, the analysis showed that commodity ETF allocation benefited most from conservative and downside-risk-aware optimization, while optimized portfolios continued to require explicit tail-risk and implementation diagnostics.
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