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Multi-frequency downside risk interconnectedness between soft agricultural commodities

Author

Listed:
  • Dejan Živkov

    (Novi Sad School of Business, University of Novi Sad, Novi Sad, Serbia)

  • Boris Kuzman

    (Institute of Agricultural Economics, Belgrade, Serbia)

  • Jonel Subić

    (Institute of Agricultural Economics, Belgrade, Serbia)

Abstract

In this article, we explore multiscale extreme risk interdependence between four soft agricultural markets - coffee, cocoa, cotton and orange juice. Wavelet correlation and cross-correlation are used to investigate this interlink, and dynamic conditional Value at Risk is used to measure extreme risk. Wavelet correlation results suggest a very weak connection between the markets in the short-term and midterm horizons, which means that investors who operate in the short term or midterm do not have to apply hedging measures against extreme risk. However, the situation is different in the long term, where relatively high correlations are found on the highest wavelet scale in all pairs, except coffee-cocoa. Complementary cross-correlation analysis indicates a lead-lag relationship between the markets. The results are mostly in line with expectations, as bigger markets lead smaller markets. Only in the cases of cocoa-cotton and cocoa-orange juice does the opposite happen.

Suggested Citation

  • Dejan Živkov & Boris Kuzman & Jonel Subić, 2023. "Multi-frequency downside risk interconnectedness between soft agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(8), pages 332-342.
  • Handle: RePEc:caa:jnlage:v:69:y:2023:i:8:id:125-2023-agricecon
    DOI: 10.17221/125/2023-AGRICECON
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    References listed on IDEAS

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    1. Cornelis Gardebroek & Manuel A. Hernandez & Miguel Robles, 2016. "Market interdependence and volatility transmission among major crops," Agricultural Economics, International Association of Agricultural Economists, vol. 47(2), pages 141-155, March.
    2. J.E. Boscá & R. Doménech & J. Ferri & J.R. García & C. Ulloa, 2021. "The stabilizing effects of economic policies in Spain in times of COVID-19," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 29(85), pages 4-20, January.
    3. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    4. Zaghum Umar & Dennis Olson, 2022. "Strategic asset allocation and the demand for real estate: international evidence," Empirical Economics, Springer, vol. 62(5), pages 2461-2513, May.
    5. Gema Fernández-Avilés & José-María Montero & Lidia Sanchis-Marco, 2020. "Extreme downside risk co-movement in commodity markets during distress periods: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 26(12), pages 1207-1237, August.
    6. Dejan Živkov & Jasmina Đurašković & Marina Gajić‐Glamočlija, 2022. "Multiscale downside risk interdependence between the major agricultural commodities," Agribusiness, John Wiley & Sons, Ltd., vol. 38(4), pages 990-1011, October.
    7. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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