IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/61327.html
   My bibliography  Save this paper

Futures Commodities Prices and Media Coverage

Author

Listed:
  • Almanzar, Miguel
  • Torero, Maximo
  • von Grebmer, Klaus

Abstract

In this paper we examine the effects of media coverage of commodity prices increases and decreases on the price of the commodity and how media coverage in other commodities affects prices. We provide evidence of the relationship between media coverage and its intensity to the price level of agricultural commodities and oil futures. We find that price movements are correlated with the media coverage of up movements, or increase in prices. The direction of the correlation is robust and positive for media coverage of increases in prices, and negative for decreases in prices. These results point to increases in prices being exacerbated by media attention by 8%. In addition, we find interesting countervailing effects of this reinforcing price pressures due to media activity in the previous days. Finally, we find that even though volatility is higher for the set of days where there is media coverage, this hides important dynamics between media coverage and volatility. The volatility of market adjusted returns is negatively correlated with the media coverage, both up and down media coverage. Markets days with intense media coverage of commodity prices tends to have lower volatility.

Suggested Citation

  • Almanzar, Miguel & Torero, Maximo & von Grebmer, Klaus, 2014. "Futures Commodities Prices and Media Coverage," MPRA Paper 61327, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:61327
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/61327/1/MPRA_paper_61327.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Thompson, Samuel B., 2011. "Simple formulas for standard errors that cluster by both firm and time," Journal of Financial Economics, Elsevier, vol. 99(1), pages 1-10, January.
    2. Ohlson, James A. & Penman, Stephen H., 1985. "Volatility increases subsequent to stock splits: An empirical aberration," Journal of Financial Economics, Elsevier, vol. 14(2), pages 251-266, June.
    3. Wolfram Schlenker & Sofia B. Villas-Boas, 2009. "Consumer and Market Responses to Mad Cow Disease," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(4), pages 1140-1152.
    4. Han, Chirok & Phillips, Peter C. B., 2010. "Gmm Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity," Econometric Theory, Cambridge University Press, vol. 26(1), pages 119-151, February.
    5. Randal R. Rucker & Walter N. Thurman & Jonathan K. Yoder, 2005. "Estimating the Structure of Market Reaction to News: Information Events and Lumber Futures Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 482-500.
    6. Dubofsky, David A, 1991. "Volatility Increases Subsequent to NYSE and AMEX Stock Splits," Journal of Finance, American Finance Association, vol. 46(1), pages 421-431, March.
    7. Colin A. Carter & Aaron Smith, 2007. "Estimating the Market Effect of a Food Scare: The Case of Genetically Modified StarLink Corn," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 522-533, August.
    8. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    9. Stephen W. Pruitt & Wuttipan Tawarangkoon & K. C. John Wei, 1987. "Chernobyl, commodities, and chaos: An examination of the reaction of commodity futures prices to evolving information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 7(5), pages 555-569, October.
    10. Carter, Colin A. & Smith, Aaron D., 2004. "The Market Effect of a Food Scare: The Case of Genetically Modified StarLink Corn," Working Papers 11997, University of California, Davis, Department of Agricultural and Resource Economics.
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    13. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    14. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    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. Matthias Kalkuhl & Lukas Kornher & Marta Kozicka & Pierre Boulanger & Maximo Torero, 2013. "Conceptual framework on price volatility and its impact on food and nutrition security in the short term," FOODSECURE Working papers 15, LEI Wageningen UR.

    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. Almanzar, Miguel & Torero, Maximo, 2017. "Media Coverage and Food Commodities: Agricultural Futures Prices and Volatility Effects," Discussion Papers 264781, University of Bonn, Center for Development Research (ZEF).
    2. Bao, Yong & Yu, Xuewen, 2023. "Indirect inference estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1027-1053.
    3. Chihwa Kao & Long Liu & Rui Sun, 2021. "A bias-corrected fixed effects estimator in the dynamic panel data model," Empirical Economics, Springer, vol. 60(1), pages 205-225, January.
    4. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    5. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.
    6. Joakim Westerlund & Jörg Breitung, 2013. "Lessons from a Decade of IPS and LLC," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 547-591, August.
    7. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    8. Gouriéroux, Christian & Phillips, Peter C.B. & Yu, Jun, 2010. "Indirect inference for dynamic panel models," Journal of Econometrics, Elsevier, vol. 157(1), pages 68-77, July.
    9. Nabamita Dutta & Deepraj Mukherjee, 2018. "Can financial development enhance transparency?," Economic Change and Restructuring, Springer, vol. 51(4), pages 279-302, November.
    10. Westerlund, Joakim & Norkute, Milda, 2014. "A Factor Analytical Method to Interactive Effects Dynamic Panel Models with or without Unit Root," Working Papers 2014:12, Lund University, Department of Economics.
    11. John C. Chao & Peter C. B. Phillips, 2019. "Uniform Inference in Panel Autoregression," Econometrics, MDPI, vol. 7(4), pages 1-28, November.
    12. Hagen, Tobias & Waldeck, Stefanie, 2014. "Using panel econometric methods to estimate the effect of milk consumption on the mortality rate of prostate and ovarian cancer," Working Paper Series 03, Frankfurt University of Applied Sciences, Faculty of Business and Law.
    13. Li, Rui & Wan, Alan T.K. & You, Jinhong, 2016. "Semiparametric GMM estimation and variable selection in dynamic panel data models with fixed effects," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 401-423.
    14. Youngho Kang & Byung-Yeon Kim, 2018. "Immigration and economic growth: do origin and destination matter?," Applied Economics, Taylor & Francis Journals, vol. 50(46), pages 4968-4984, October.
    15. Huy Quang Doan, 2019. "Trade, Institutional Quality and Income: Empirical Evidence for Sub-Saharan Africa," Economies, MDPI, vol. 7(2), pages 1-23, May.
    16. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    17. Castelló-Climent, Amparo & Mukhopadhyay, Abhiroop, 2013. "Mass education or a minority well educated elite in the process of growth: The case of India," Journal of Development Economics, Elsevier, vol. 105(C), pages 303-320.
    18. Guglielmo Maria Caporale & Anamaria Diana Sova & Robert Sova, 2024. "The Covid‐19 pandemic and European trade flows: Evidence from a dynamic panel model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2563-2580, July.
    19. Zheng, Xinye & Li, Fanghua & Song, Shunfeng & Yu, Yihua, 2013. "Central government's infrastructure investment across Chinese regions: A dynamic spatial panel data approach," China Economic Review, Elsevier, vol. 27(C), pages 264-276.
    20. Mohammad Ziaul Hoque & MD. Rabiul Islam & Mohammad Nurul Azam, 2013. "Board Committee Meetings and Firm Financial Performance: An Investigation of Australian Companies," International Review of Finance, International Review of Finance Ltd., vol. 13(4), pages 503-528, December.

    More about this item

    Keywords

    Prices volatility Media Foodsecurity;

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • G1 - Financial Economics - - General Financial Markets
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture

    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:pra:mprapa:61327. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.