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Dependence risk analysis in energy, agricultural and precious metals commodities: A pair vine copula approach

Author

Listed:
  • Satish Kumar

    (ICFAI Foundation for Higher Education, India)

  • Aviral K. Tiwari

    (Montpellier Business School, Montpellier, France)

  • Ibrahim D. Raheem

    (EXCAS, Liège, Belgium)

  • Qiang Ji

    (Beijing, China)

Abstract

We apply pair vine copulas, specifically the C-vine and R-vine copulas, to examine the conditional multivariate dependence pattern/structure and R-vine copula-based value-at-risk (VaR) to assess financial portfolio risk. We examine the co-dependencies of 13 major commodity markets (which include three energy commodities, six agricultural commodities and four precious metals prices) from 2 January 2003 to 19 December 2016. Dividing our sample into three sub-periods, namely pre-GFC, GFC and post-GFC, we find that the dependencies among commodities undergo changes in a complex manner, changing in different financial conditions, and that the Student-t copula appears on the maximum number of occasions, especially during the GFC period, signifying the existence of fatter tails in the distributions of returns. We further show that the co-dependencies computed using R-vine copulas are best suited to compute the portfolio VaR during the considered time period.

Suggested Citation

  • Satish Kumar & Aviral K. Tiwari & Ibrahim D. Raheem & Qiang Ji, 2019. "Dependence risk analysis in energy, agricultural and precious metals commodities: A pair vine copula approach," Research Africa Network Working Papers 19/092, Research Africa Network (RAN).
  • Handle: RePEc:abh:wpaper:19/092
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    1. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    2. Antonakakis, Nikolaos & Chang, Tsangyao & Cunado, Juncal & Gupta, Rangan, 2018. "The relationship between commodity markets and commodity mutual funds: A wavelet-based analysis," Finance Research Letters, Elsevier, vol. 24(C), pages 1-9.
    3. Arreola Hernandez, Jose, 2014. "Are oil and gas stocks from the Australian market riskier than coal and uranium stocks? Dependence risk analysis and portfolio optimization," Energy Economics, Elsevier, vol. 45(C), pages 528-536.
    4. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    5. Fernandez-Perez, Adrian & Frijns, Bart & Tourani-Rad, Alireza, 2016. "Contemporaneous interactions among fuel, biofuel and agricultural commodities," Energy Economics, Elsevier, vol. 58(C), pages 1-10.
    6. Nicola, Francesca de & De Pace, Pierangelo & Hernandez, Manuel A., 2016. "Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment," Energy Economics, Elsevier, vol. 57(C), pages 28-41.
    7. David E. Allen & Michael McAleer & Abhay K. Singh, 2017. "Risk Measurement and Risk Modelling Using Applications of Vine Copulas," Sustainability, MDPI, vol. 9(10), pages 1-34, September.
    8. Shahzad, Syed Jawad Hussain & Hernandez, Jose Arreola & Al-Yahyaee, Khamis Hamed & Jammazi, Rania, 2018. "Asymmetric risk spillovers between oil and agricultural commodities," Energy Policy, Elsevier, vol. 118(C), pages 182-198.
    9. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    10. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    11. Mensi, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong & Yoon, Seong-Min, 2014. "Dynamic spillovers among major energy and cereal commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 225-243.
    12. Ji, Qiang & Fan, Ying, 2016. "How do China's oil markets affect other commodity markets both domestically and internationally?," Finance Research Letters, Elsevier, vol. 19(C), pages 247-254.
    13. Lahmiri, Salim & Uddin, Gazi Salah & Bekiros, Stelios, 2017. "Clustering of short and long-term co-movements in international financial and commodity markets in wavelet domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 947-955.
    14. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    15. HEINEN, Andréas & VALDESOGO, Alfonso, 2009. "Asymmetric CAPM dependence for large dimensions: the Canonical Vine Autoregressive Model," LIDAM Discussion Papers CORE 2009069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    16. Mensi, Walid & Hkiri, Besma & Al-Yahyaee, Khamis H. & Kang, Sang Hoon, 2018. "Analyzing time–frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 74-102.
    17. Aloui, Riadh & Ben Aïssa, Mohamed Safouane & Nguyen, Duc Khuong, 2013. "Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 719-738.
    18. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    19. Nazlioglu, Saban & Soytas, Ugur, 2012. "Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis," Energy Economics, Elsevier, vol. 34(4), pages 1098-1104.
    20. Ji, Qiang & Fan, Ying, 2016. "Evolution of the world crude oil market integration: A graph theory analysis," Energy Economics, Elsevier, vol. 53(C), pages 90-100.
    21. Bing-Yue Liu & Qiang Ji & Ying Fan, 2017. "A new time-varying optimal copula model identifying the dependence across markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 437-453, March.
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    Cited by:

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    6. Shahzad, Farrukh & Bouri, Elie & Mokni, Khaled & Ajmi, Ahdi Noomen, 2021. "Energy, agriculture, and precious metals: Evidence from time-varying Granger causal relationships for both return and volatility," Resources Policy, Elsevier, vol. 74(C).
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    8. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    9. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2020. "Spillovers and co-movements between precious metals and energy markets: Implications on portfolio management," Resources Policy, Elsevier, vol. 69(C).

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    More about this item

    Keywords

    R-vine; VaR; Dependence structure; Tree structure; Commodity markets;
    All these keywords.

    JEL classification:

    • F3 - International Economics - - International Finance
    • G1 - Financial Economics - - General Financial Markets

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