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Economic cycles and downside commodities risk

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  • Robert J. Powell
  • Duc H. Vo
  • Thach N. Pham

Abstract

We de-compose the S&P Goldman Sachs Commodity Index into its underlying commodity sub-categories and develop a modified conditional value at risk (CVaR) metric to examine downside risk linked to economic periods which are classified by their GDP growth as green, yellow, orange and red. We term this new metric economic CVaR (ECVaR). We found significant differences in the relative ECVaR rankings of different commodities over our different economic cycles.

Suggested Citation

  • Robert J. Powell & Duc H. Vo & Thach N. Pham, 2018. "Economic cycles and downside commodities risk," Applied Economics Letters, Taylor & Francis Journals, vol. 25(4), pages 258-263, February.
    • Handle: RePEc:taf:apeclt:v:25:y:2018:i:4:p:258-263
    DOI: 10.1080/13504851.2017.1316818
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    References listed on IDEAS

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    1. D. E. Allen & M. McAleer & R. J. Powell & A. K. Singh, 2016. "A capital adequacy buffer model," Applied Economics Letters, Taylor & Francis Journals, vol. 23(3), pages 175-179, February.
    2. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    3. Ratti, Ronald A & Vespignani, Joaquin L., 2012. "Why are crude oil prices high when global activity is weak?," MPRA Paper 43777, University Library of Munich, Germany.
    4. Annastiina Silvennoinen & Susan Thorp, 2016. "Crude Oil and Agricultural Futures: An Analysis of Correlation Dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(6), pages 522-544, June.
    5. David Edmund Allen & Robert John Powell & Abhay Kumar Singh, 2012. "Beyond reasonable doubt: multiple tail risk measures applied to European industries," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 671-676, May.
    6. Paul Embrechts & Giovanni Puccetti & Ludger Rüschendorf & Ruodu Wang & Antonela Beleraj, 2014. "An Academic Response to Basel 3.5," Risks, MDPI, vol. 2(1), pages 1-24, February.
    7. Zhang, Zibin & Lohr, Luanne & Escalante, Cesar & Wetzstein, Michael, 2010. "Food versus fuel: What do prices tell us?," Energy Policy, Elsevier, vol. 38(1), pages 445-451, January.
    8. Boubaker, Heni & Sghaier, Nadia, 2013. "Portfolio optimization in the presence of dependent financial returns with long memory: A copula based approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 361-377.
    9. Carole Toque & Virginie Terraza, 2014. "Histogram-valued data on value at risk measures: a symbolic approach for risk attribution," Applied Economics Letters, Taylor & Francis Journals, vol. 21(17), pages 1243-1251, November.
    10. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    11. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    12. Ron Bird & Harry Liem & Susan Thorp, 2012. "The Tortoise and the Hare: Risk Premium Versus Alternative Asset Portfolios," Working Paper Series 16, The Paul Woolley Centre for Capital Market Dysfunctionality, University of Technology, Sydney.
    13. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.
    14. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Why are crude oil prices high when global activity is weak?," Economics Letters, Elsevier, vol. 121(1), pages 133-136.
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    Cited by:

    1. Ngoc Phu Tran & Thang Cong Nguyen & Duc Hong Vo & Michael McAleer, 2019. "Market Risk Analysis of Energy in Vietnam," Risks, MDPI, vol. 7(4), pages 1-13, November.
    2. Duc Hong Vo & Quang Van Tuan & Trung Vu-Thanh Pham, 2019. "Sectoral Risks in Vietnam and Malaysia A Comparative Analysis," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 62-87, March.
    3. Duc Hong Vo, 2021. "Portfolio Optimization and Diversification in China: Policy Implications for Vietnam and Other Emerging Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(1), pages 223-238, January.
    4. Guhathakurta, Kousik & Dash, Saumya Ranjan & Maitra, Debasish, 2020. "Period specific volatility spillover based connectedness between oil and other commodity prices and their portfolio implications," Energy Economics, Elsevier, vol. 85(C).
    5. Hoang Huy Nguyen & Chi Minh Ho & Duc Hong Vo, 2019. "An Empirical Test of Capital Structure Theories for the Vietnamese Listed Firms," JRFM, MDPI, vol. 12(3), pages 1-11, September.
    6. Ray-Ming Chen, 2022. "Economic Categorizing Based on DFT-induced Supervised Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 125-150, January.

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