IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v48y2015i4p1691-1712.html
   My bibliography  Save this article

GARCH modeling of five popular commodities

Author

Listed:
  • Saralees Nadarajah
  • Emmanuel Afuecheta
  • Stephen Chan

Abstract

Flexible models for the innovation process of GARCH models have been limited. Here, we show the flexibility of two recently proposed distributions due to Zhu and Zinde-Walsh (J Econom 148:86–99, 2009 ) and Zhu and Galbraith (J Econom 157:297–305, 2010 ) by means of GARCH modeling of five popular commodities. The five commodities considered are Cocoa bean, Brent crude oil, West Texas intermediate crude oil, Gold and Silver. For each commodity, one of the two models due to Zhu and Zinde-Walsh ( 2009 ) and Zhu and Galbraith ( 2010 ) is shown to perform better than those commonly known. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "GARCH modeling of five popular commodities," Empirical Economics, Springer, vol. 48(4), pages 1691-1712, June.
    • Handle: RePEc:spr:empeco:v:48:y:2015:i:4:p:1691-1712
    DOI: 10.1007/s00181-014-0845-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00181-014-0845-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00181-014-0845-3?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. Salisu, Afees A. & Fasanya, Ismail O., 2013. "Modelling oil price volatility with structural breaks," Energy Policy, Elsevier, vol. 52(C), pages 554-562.
    2. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    3. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    4. Gregory-Allen, Russell & Lu, Helen & Stork, Philip, 2012. "Asymmetric extreme tails and prospective utility of momentum returns," Economics Letters, Elsevier, vol. 117(1), pages 295-297.
    5. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    6. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    7. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    8. Abdolreza Yazdani-Chamzini & Siamak Haji Yakhchali & Diana Volungevičienė & Edmundas Kazimieras Zavadskas, 2012. "Forecasting gold price changes by using adaptive network fuzzy inference system," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(5), pages 994-1010, April.
    9. Filip Novotný, 2012. "The Link Between the Brent Crude Oil Price and the US Dollar Exchange Rate," Prague Economic Papers, Prague University of Economics and Business, vol. 2012(2), pages 220-232.
    10. Cheng, Wan-Hsiu & Hung, Jui-Cheng, 2011. "Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 160-173, January.
    11. Ubilava, David & Helmers, Claes Gustav, 2011. "The ENSO Impact on Predicting World Cocoa Prices," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103528, Agricultural and Applied Economics Association.
    12. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    13. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    14. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    15. Zhu, Dongming & Zinde-Walsh, Victoria, 2009. "Properties and estimation of asymmetric exponential power distribution," Journal of Econometrics, Elsevier, vol. 148(1), pages 86-99, January.
    16. Hsu-Ling Chang & Chi-Wei Su, 2010. "The relationship between the Vietnam stock market and its major trading partners - TECM with bivariate asymmetric GARCH model," Applied Economics Letters, Taylor & Francis Journals, vol. 17(13), pages 1279-1283.
    17. Acatrinei, Marius & Gorun, Adrian & Marcu, Nicu, 2013. "A DCC-GARCH Model To Estimate the Risk to the Capital Market in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 136-148, March.
    18. Deimante Teresiene, 2009. "Lithuanian stock market analysis using a set of Garch models," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 10(4), pages 349-360, August.
    19. Yen-Hsien Lee & Ya-Ling Huang & Hao-Jang Yang, 2012. "The Asymmetric Long-Run Relationship Between Crude Oil And Gold Futures," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 6(1), pages 9-15.
    20. Lee, Chien-Chiang & Zeng, Jhih-Hong, 2011. "Revisiting the relationship between spot and futures oil prices: Evidence from quantile cointegrating regression," Energy Economics, Elsevier, vol. 33(5), pages 924-935, September.
    21. Shu-Chen Chang & Chung-Hua Shen, 2011. "The effect of exchange-rate uncertainty on unemployment in three developing Asian countries: evidence from bivariate GARCH approach," Applied Economics Letters, Taylor & Francis Journals, vol. 18(8), pages 783-788.
    22. Arthur De Vany & W. David Walls, 2002. "Does Hollywood Make Too Many R-Rated Movies? Risk, Stochastic Dominance, and the Illusion of Expectation," The Journal of Business, University of Chicago Press, vol. 75(3), pages 425-452, July.
    23. Nakamura, Tomomichi & Small, Michael, 2007. "Tests of the random walk hypothesis for financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(2), pages 599-615.
    24. Yen-Hsien Lee & Chien-Liang Chiu, 2011. "Arbitrage behaviour in the exchange rates of Taiwan and Japan: applying the smooth transition vector error correction model with GJR-GARCH and spillover volatility," Applied Economics, Taylor & Francis Journals, vol. 43(15), pages 1935-1943.
    25. Reboredo, Juan C., 2013. "Is gold a hedge or safe haven against oil price movements?," Resources Policy, Elsevier, vol. 38(2), pages 130-137.
    26. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
    27. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    28. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
    29. Maurice, Noemie & Davis, Junior, 2011. "Unravelling the underlying causes of price volatility in world coffee and cocoa commodity markets," MPRA Paper 43813, University Library of Munich, Germany, revised 2012.
    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. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    2. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    3. Peng, Zuoxiang & Li, Chunqiao & Nadarajah, Saralees, 2016. "Extremal properties of the skew-t distribution," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 10-19.
    4. Rajaeifar, Mohammad Ali & Ghanavati, Hossein & Dashti, Behrouz B. & Heijungs, Reinout & Aghbashlo, Mortaza & Tabatabaei, Meisam, 2017. "Electricity generation and GHG emission reduction potentials through different municipal solid waste management technologies: A comparative review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 414-439.
    5. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie & Ji, Qiang, 2020. "The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach," Research in International Business and Finance, Elsevier, vol. 54(C).

    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. Emmanuel Afuecheta & Idika E. Okorie & Saralees Nadarajah & Geraldine E. Nzeribe, 2024. "Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 271-304, January.
    2. Walid Chkili, 2015. "Gold–oil prices co-movements and portfolio diversification implications," Economics Bulletin, AccessEcon, vol. 35(4), pages 2832-2845.
    3. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    4. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    5. Victor Korolev, 2023. "Analytic and Asymptotic Properties of the Generalized Student and Generalized Lomax Distributions," Mathematics, MDPI, vol. 11(13), pages 1-27, June.
    6. Robina Iqbal & Ghulam Sorwar & Rose Baker & Taufiq Choudhry, 2020. "Multiday expected shortfall under generalized t distributions: evidence from global stock market," Review of Quantitative Finance and Accounting, Springer, vol. 55(3), pages 803-825, October.
    7. Miroslava Zavadska & Lucía Morales & Joseph Coughlan, 2018. "The Lead–Lag Relationship between Oil Futures and Spot Prices—A Literature Review," IJFS, MDPI, vol. 6(4), pages 1-22, October.
    8. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    9. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    10. Kanjilal, Kakali & Ghosh, Sajal, 2017. "Dynamics of crude oil and gold price post 2008 global financial crisis – New evidence from threshold vector error-correction model," Resources Policy, Elsevier, vol. 52(C), pages 358-365.
    11. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    12. Gil-Alana, Luis A. & Yaya, OlaOluwa S. & Awe, Olushina O., 2017. "Time series analysis of co-movements in the prices of gold and oil: Fractional cointegration approach," Resources Policy, Elsevier, vol. 53(C), pages 117-124.
    13. Nadarajah, Saralees & Chan, Stephen & Afuecheta, Emmanuel, 2013. "On the characteristic function for asymmetric Student t distributions," Economics Letters, Elsevier, vol. 121(2), pages 271-274.
    14. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
    15. Chkili, Walid, 2015. "Gold-oil prices co-movements and portfolio diversification implications," MPRA Paper 68110, University Library of Munich, Germany.
    16. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    17. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    18. 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.
    19. Herrera, Rodrigo & Rodriguez, Alejandro & Pino, Gabriel, 2017. "Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model," Energy Economics, Elsevier, vol. 63(C), pages 129-143.
    20. Huang, Jianbai & Dong, Xuesong & Chen, Jinyu & Zhong, Meirui, 2022. "Do oil prices and economic policy uncertainty matter for precious metal returns? New insights from a TVP-VAR framework," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 433-445.

    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:spr:empeco:v:48:y:2015:i:4:p:1691-1712. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.