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Measuring the risk-adjusted performance of selected 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 paper, we used several elaborate return-to-risk methods to investigate the risk-adjusted performances of five soft commodities. Regarding only the level of risk, we found that cocoa had the highest risk of losses, followed by orange juice. Cotton and coffee had the lowest risk of losses. However, according to the return-to-risk output, cotton was the worst asset in which to invest because it had negative average returns. In contradistinction, sugar had a relatively high risk of losses but also the highest average returns, which put it in the first place according to the Sharpe, Sortino and modified Sharpe ratios. Although orange juice had the second-worst downside risk performance, it came in second place according to the return-to-risk ratio because it had relatively high average returns.

Suggested Citation

  • Dejan Živkov & Boris Kuzman & Jonel Subić, 2022. "Measuring the risk-adjusted performance of selected soft agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(3), pages 87-96.
    • Handle: RePEc:caa:jnlage:v:68:y:2022:i:3:id:298-2021-agricecon
    DOI: 10.17221/298/2021-AGRICECON
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    References listed on IDEAS

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    1. Marta Guth & Katarzyna Smędzik-Ambroży, 2020. "Economic resources versus the efficiency of different types of agricultural production in regions of the European union," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 1036-1051, January.
    2. Yijin He & Tadahiro Nakajima & Shigeyuki Hamori, 2020. "Can Brics’S Currency Be A Hedge Or A Safe Haven For Energy Portfolio? An Evidence From Vine Copula Approach," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 65(04), pages 805-836, June.
    3. Tereza Palanska, 2020. "Measurement of Volatility Spillovers and Asymmetric Connectedness on Commodity and Equity Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 70(1), pages 42-69, February.
    4. Emrah ALTUN & Morad ALIZADEH & Gamze OZEL & Hüseyin TATLIDIL & Najmieh MAKSAYI, 2017. "Forecasting Value-At-Risk With Two-Step Method: Garch-Exponentiated Odd Log-Logistic Normal Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 97-115, December.
    5. Joseph P Janzen & Aaron Smith & Colin A Carter, 2018. "Commodity Price Comovement and Financial Speculation: The Case of Cotton," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 264-285.
    6. Dejan Živkov & Jovan Njegić & Jasmina Pavlović, 2016. "Dynamic Correlation Between Stock Returns And Exchange Rate And Its Dependence On The Conditional Volatilities – The Case Of Several Eastern European Countries," Bulletin of Economic Research, Wiley Blackwell, vol. 68(S1), pages 28-41, December.
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