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Modelling general dependence between commodity forward curves

Author

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  • Zolotko, Mikhail
  • Okhrin, Ostap

Abstract

This study proposes a novel framework for the joint modelling of commodity forward curves. Its key contribution is twofold. First, dynamic correlation models are applied in this context as part of the modelling scheme. Second, we introduce a family of dynamic conditional correlation models based on hierarchical Archimedean copulae (HAC DCC), which are flexible, but parsimonious instruments that capture a wide range of dynamic dependencies. The conducted analysis allows us to obtain precise out-of-sample forecasts of the distribution of the returns of various commodity futures portfolios. The Value-at-Risk analysis shows that HAC DCC models outperform other introduced benchmark models on a consistent basis.

Suggested Citation

  • Zolotko, Mikhail & Okhrin, Ostap, 2012. "Modelling general dependence between commodity forward curves," SFB 649 Discussion Papers 2012-060, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2012-060
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    Cited by:

    1. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    2. Yang, Lu & Cai, Xiao Jing & Li, Mengling & Hamori, Shigeyuki, 2015. "Modeling dependence structures among international stock markets: Evidence from hierarchical Archimedean copulas," Economic Modelling, Elsevier, vol. 51(C), pages 308-314.
    3. Wanat, Stanisław & Papież, Monika & Śmiech, Sławomir, 2014. "The conditional dependence structure between precious metals: a copula-GARCH approach," MPRA Paper 56664, University Library of Munich, Germany.
    4. Mark Higgins, 2017. "A Two Factor Forward Curve Model with Stochastic Volatility for Commodity Prices," Papers 1708.01665, arXiv.org, revised Aug 2017.
    5. Monika Papież & Stanisław Wanat & Sławomir Śmiech, 2016. "In Search of Hedges and Safe Havens in Global Financial Markets," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(3), pages 557-574, September.
    6. Ouyang, Zi-sheng & Liu, Meng-tian & Huang, Su-su & Yao, Ting, 2022. "Does the source of oil price shocks matter for the systemic risk?," Energy Economics, Elsevier, vol. 109(C).
    7. Tamakoshi, Go & Hamori, Shigeyuki, 2014. "The conditional dependence structure of insurance sector credit default swap indices," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 122-132.
    8. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2019. "Quantifying Risk in Traditional Energy and Sustainable Investments," Sustainability, MDPI, vol. 11(3), pages 1-22, January.

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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