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Power ARCH modelling of commodity futures data on the London Metal Exchange

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  • Michael McKenzie
  • Heather Mitchell
  • Robert Brooks
  • Robert Faff

Abstract

A recent addition to the ARCH family of econometric models was introduced by Ding and co-workers wherein the power term by which the data is transformed was estimated within the model rather than being imposed by the researcher. This paper considers the ability of the Power GARCH class of models to capture the stylized features of volatility in a range of commodity futures prices traded on the London Metals Exchange (LME). The results of this procedure suggest that asymmetric effects are not generally present in the LME futures data. Further, unlike stock market data which is well described by the model, futures data is not as well described by the APGARCH model. Nested within the APGARCH model are several other models from the ARCH family. This paper uses the standard log likelihood procedure to conduct pairwise comparisons of the relative merits of each and the results suggest that it is the Taylor GARCH model which performs best.

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  • Michael McKenzie & Heather Mitchell & Robert Brooks & Robert Faff, 2001. "Power ARCH modelling of commodity futures data on the London Metal Exchange," The European Journal of Finance, Taylor & Francis Journals, vol. 7(1), pages 22-38.
    • Handle: RePEc:taf:eurjfi:v:7:y:2001:i:1:p:22-38
    DOI: 10.1080/13518470123011
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    Cited by:

    1. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    2. Sinha, Pankaj & Mathur, Kritika, 2013. "A study on the Price Behavior of Base Metals traded in India," MPRA Paper 47028, University Library of Munich, Germany.
    3. Hammoudeh, Shawkat M. & Yuan, Yuan & McAleer, Michael & Thompson, Mark A., 2010. "Precious metals-exchange rate volatility transmissions and hedging strategies," International Review of Economics & Finance, Elsevier, vol. 19(4), pages 633-647, October.
    4. Gaye Hatice Gencer & Zafer Musoglu, 2014. "Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 5(2), pages 87-101, April.
    5. Qiang Xia & Heung Wong & Jinshan Liu & Rubing Liang, 2017. "Bayesian Analysis of Power-Transformed and Threshold GARCH Models: A Griddy-Gibbs Sampler Approach," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 353-372, October.
    6. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    7. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    8. Nidhi Choudhary & Girish K. Nair & Harsh Purohit, 2015. "Volatility In Copper Prices In India," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-26, December.
    9. Perry Sadorsky & Michael D. McKenzie, 2008. "Power transformation models and volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 587-606.
    10. Jin, Jiayu & Han, Liyan & Wu, Lei & Zeng, Hongchao, 2020. "The hedging effect of green bonds on carbon market risk," International Review of Financial Analysis, Elsevier, vol. 71(C).
    11. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
    12. Nikhil Kaushik, 2018. "Do global oil price shocks affect Indian metal market?," Energy & Environment, , vol. 29(6), pages 891-904, September.
    13. Dominik Boos, 2024. "Risky times: Seasonality and event risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 767-783, May.
    14. Clinton Watkins & Michael McAleer, 2004. "Econometric modelling of non‐ferrous metal prices," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 651-701, December.
    15. Chevallier, Julien & Ielpo, Florian, 2017. "Investigating the leverage effect in commodity markets with a recursive estimation approach," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 763-778.
    16. Ane, Thierry, 2006. "An analysis of the flexibility of Asymmetric Power GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1293-1311, November.

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    More about this item

    Keywords

    Power Arch London Metal Exchange Futures;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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