IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v68y2005i5p567-579.html
   My bibliography  Save this article

Related commodity markets and conditional correlations

Author

Listed:
  • Watkins, Clinton
  • McAleer, Michael

Abstract

Related commodity markets have two characteristics: (i) they may be expected to follow similar volatility processes; (ii) such markets are frequently represented by a market aggregate or index. Indices are used to represent the performance and aggregate time series properties of a group of markets. An important issue regarding the time series properties of an index is how the index reflects the corresponding properties of its components, particularly with regard to volatility and risk. This paper investigates the volatility of a market index relative to the volatility of its underlying assets by analysing correlation matrices derived from rolling AR(1)-generalised autoregressive conditional heteroskedasticity (GARCH)(1,1) model estimates. The second moment properties of a linear aggregate of ARMA processes with GARCH errors are analysed and compared with the properties of the individual returns series. Empirical application is made to the markets for non-ferrous metals on the London Metal Exchange (LME). The volatility of the LME Base Metals Index (LMEX) is modelled and compared with the volatility of the 3-month futures contracts for aluminium, copper, lead, nickel, tin, and zinc.

Suggested Citation

  • Watkins, Clinton & McAleer, Michael, 2005. "Related commodity markets and conditional correlations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 567-579.
    • Handle: RePEc:eee:matcom:v:68:y:2005:i:5:p:567-579
    DOI: 10.1016/j.matcom.2005.02.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475405000479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2005.02.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(3), pages 722-729, June.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. He, Changli & Terasvirta, Timo, 1999. "Properties of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 92(1), pages 173-192, September.
    4. Ling, Shiqing & McAleer, Michael, 2002. "Stationarity and the existence of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 109-117, January.
    5. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zhao, Yiran & Gao, Xiangyun & An, Haizhong & Xi, Xian & Sun, Qingru & Jiang, Meihui, 2020. "The effect of the mined cobalt trade dependence Network's structure on trade price," Resources Policy, Elsevier, vol. 65(C).
    2. John T. Cuddington & Arturo L. Va'squez Cordano, 2013. "Linkages between spot and futures prices: Tests of the Fama-French-Samuelson hypotheses," Working Papers 2013-09, Colorado School of Mines, Division of Economics and Business.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    2. Amélie Charles & Olivier Darné, 2019. "The accuracy of asymmetric GARCH model estimation," International Economics, CEPII research center, issue 157, pages 179-202.
    3. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 139(2), pages 259-284, August.
    4. Francq, Christian & Zakoïan, Jean-Michel, 2022. "Testing the existence of moments for GARCH processes," Journal of Econometrics, Elsevier, vol. 227(1), pages 47-64.
    5. Li, Ming-Yuan Leon, 2008. "Clarifying the dynamics of the relationship between option and stock markets using the threshold vector error correction model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 511-520.
    6. Felix Chan & Michael McAleer & Marcelo C. Medeiros, 2015. "Structure and asymptotic theory for nonlinear models with GARCH erros," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 16(1), pages 1-21.
    7. Dora Marinova & Michael McAleer, 2002. "Trends and volatility in Japanese patenting in the USA: An analysis of the electronics and transport industries," Scientometrics, Springer;Akadémiai Kiadó, vol. 55(2), pages 171-187, August.
    8. W. K. Li & Shiqing Ling & Michael McAleer, 2001. "A Survey of Recent Theoretical Results for Time Series Models with GARCH Errors," ISER Discussion Paper 0545, Institute of Social and Economic Research, Osaka University.
    9. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    10. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
    11. Lee, O. & Shin, D. W., 2004. "Strict stationarity and mixing properties of asymmetric power GARCH models allowing a signed volatility," Economics Letters, Elsevier, vol. 84(2), pages 167-173, August.
    12. Karanasos, Menelaos & Kim, Jinki, 2006. "A re-examination of the asymmetric power ARCH model," Journal of Empirical Finance, Elsevier, vol. 13(1), pages 113-128, January.
    13. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    14. Amélie Charles & Olivier Darné, 2019. "The accuracy of asymmetric GARCH model estimation," Post-Print hal-01943883, HAL.
    15. repec:hal:wpaper:hal-01943883 is not listed on IDEAS
    16. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    17. Massimiliano Caporin & Michael McAleer, 2011. "Thresholds, news impact surfaces and dynamic asymmetric multivariate GARCH," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(2), pages 125-163, May.
    18. Chan, Felix & Marinova, Dora & McAleer, Michael, 2004. "Modelling the asymmetric volatility of electronics patents in the USA," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 169-184.
    19. Chia-Lin Chang & Michael Mcaleer, 2009. "Daily Tourist Arrivals, Exchange Rates and Voatility for Korea and Taiwan," Korean Economic Review, Korean Economic Association, vol. 25, pages 241-267.
    20. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    21. Wan, Xiaoli & Yan, Yuruo & Zeng, Zhixiong, 2020. "Exchange rate regimes and market integration: evidence from the dynamic relations between renminbi onshore and offshore markets," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

    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:eee:matcom:v:68:y:2005:i:5:p:567-579. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.