IDEAS home Printed from https://ideas.repec.org/p/fth/melrfi/98-3.html

Power ARCH Modelling of Commodity Futures Data on the London Metal Exchange

Author

Listed:
  • McKenzie, M.
  • Michell, H.
  • Brooks, R.D.
  • Faff, R.W.

Abstract

A recent addition to the ARCH family of econometric models was introduced by Ding, Granger and Engle (1993) 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 stylised features of volatility in a range of commodity futures prices traded on the London Metals Exchange. 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.

Suggested Citation

  • McKenzie, M. & Michell, H. & Brooks, R.D. & Faff, R.W., 1998. "Power ARCH Modelling of Commodity Futures Data on the London Metal Exchange," Papers 98-3, Melbourne - Centre in Finance.
    • Handle: RePEc:fth:melrfi:98-3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. is not listed on IDEAS
    2. 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.
    3. 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.
    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. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    7. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    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. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.

    More about this item

    Keywords

    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fth:melrfi:98-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thomas Krichel (email available below). General contact details of provider: https://edirc.repec.org/data/dermiau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.