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Co-movements in commodity markets and implications in diversification benefits

Author

Listed:
  • Xiao Jing Cai

    (Kobe University)

  • Zheng Fang

    (Singapore University of Social Sciences)

  • Youngho Chang

    (Singapore University of Social Sciences)

  • Shuairu Tian

    (Shanghai Business School)

  • Shigeyuki Hamori

    (Kobe University)

Abstract

This study examines the co-movement and causality relationship between prices of crude oil, precious metals, and agricultural commodities. We use a novel approach called wavelet coherence analysis, which allows the measurement of co-movements in the time–frequency space based on the daily prices of commodities. We decompose data from September 1986 to September 2017 into 12 levels and 5 subperiods to find more generalized and convincing results. We confirm that commodity prices are in-phase and co-move. Particularly, the coherence is the largest in the long term and rises sharply in the mid-term during the crisis period. The heterogeneous directions of arrows provide strong evidence that the causality relationship between commodity prices varies over time for different frequencies. We find that the mixed commodities portfolio can provide diversification benefits in the mid-term horizons. The findings of this study can guide investors who want to benefit from diversification while investing in commodity markets.

Suggested Citation

  • Xiao Jing Cai & Zheng Fang & Youngho Chang & Shuairu Tian & Shigeyuki Hamori, 2020. "Co-movements in commodity markets and implications in diversification benefits," Empirical Economics, Springer, vol. 58(2), pages 393-425, February.
  • Handle: RePEc:spr:empeco:v:58:y:2020:i:2:d:10.1007_s00181-018-1551-3
    DOI: 10.1007/s00181-018-1551-3
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    More about this item

    Keywords

    Wavelet coherence analysis; Sharp ratio; Crude oil; Precious metals; Agricultural commodities;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F01 - International Economics - - General - - - Global Outlook
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

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