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Incorporating weather information into commodity portfolio optimization

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  • Zhang, Dongna
  • Dai, Xingyu
  • Xue, Jianhao

Abstract

This study investigates the out-of-sample performance of commodity portfolios by incorporating weather information within the Black-Litterman framework. The inclusion of weather information increases returns, reduces downside risk for energy and agricultural portfolios, and diminishes volatility in agricultural portfolios. We find significant enhancement in the efficiency of energy and agricultural portfolios with weather information. Notably, portfolios integrating low-temperature weather information outperform their counterparts across most performance measures. Our findings underscore the benefits of incorporating weather information in the optimization of commodity portfolios.

Suggested Citation

  • Zhang, Dongna & Dai, Xingyu & Xue, Jianhao, 2024. "Incorporating weather information into commodity portfolio optimization," Finance Research Letters, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324007025
    DOI: 10.1016/j.frl.2024.105672
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    Cited by:

    1. Xue, Jianhao & Dai, Xingyu & Zhang, Dongna & Nghiem, Xuan-Hoa & Wang, Qunwei, 2024. "Tail risk spillover network among green bond, energy and agricultural markets under extreme weather scenarios," International Review of Economics & Finance, Elsevier, vol. 96(PC).

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    More about this item

    Keywords

    Weather information; Energy commodity; Agricultural commodity; Portfolio optimization;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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