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Hedging with Chinese metal futures

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  • Lien, Donald
  • Yang, Li

Abstract

This paper evaluates different hedging strategies for aluminum and copper futures contracts traded at Shanghai Futures Exchange. In addition to usual candidates such as the traditional regression hedge ratio and the hedging strategy constructed from bivariate fractionally integrated generalized autoregressive conditional heteroskedasticity (BFIGARCH) model, two advanced specifications are proposed to account for impacts of the basis on market volatility and co-movements between spot and futures returns. Empirical results suggest that the basis has asymmetric effects and optimal hedging strategy constructed from the asymmetric BFIGARCH model tends to produce the best in-sample and out-of-sample hedging performance.

Suggested Citation

  • Lien, Donald & Yang, Li, 2008. "Hedging with Chinese metal futures," Global Finance Journal, Elsevier, vol. 19(2), pages 123-138.
    • Handle: RePEc:eee:glofin:v:19:y:2008:i:2:p:123-138
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    References listed on IDEAS

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    Cited by:

    1. Sang‐Kuck Chung, 2009. "Out‐of‐sample Hedge Performances for Risk Management in China Commodity Futures Markets," Asian Economic Journal, East Asian Economic Association, vol. 23(3), pages 349-372, September.
    2. Ahmed Ghorbel & Abdelwahed Trabelsi, 2012. "Optimal dynamic hedging strategy with futures oil markets via FIEGARCH-EVT copula models," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 4(1), pages 1-28.
    3. Claudio-Quiroga, Gloria & Gil-Alana, Luis A. & Maiza-Larrarte, Andoni, 2023. "Mineral prices persistence and the development of a new energy vehicle industry in China: A fractional integration approach," Resources Policy, Elsevier, vol. 82(C).
    4. Liu, Xiaochun & Jacobsen, Brian, 2011. "The Dynamic International Optimal Hedge Ratio," MPRA Paper 35260, University Library of Munich, Germany.
    5. Fu, Junhui & Zhang, Wei-Guo & Yao, Zheng & Zhang, Xili, 2012. "Hedging the portfolio of raw materials and the commodity under the mark-to-market risk," Economic Modelling, Elsevier, vol. 29(4), pages 1070-1075.
    6. repec:ocp:dbbook:9-789954-971789 is not listed on IDEAS
    7. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
    8. Liu, Qingfu & Wong, Ieokhou & An, Yunbi & Zhang, Jinqing, 2014. "Asymmetric Information and Volatility Forecasting in Commodity Futures Markets," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 79-97.

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    More about this item

    Keywords

    C13 C32 G13 Time-varying variance and correlation Long memory in volatility Dynamic hedging Chinese metal futures markets;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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