IDEAS home Printed from https://ideas.repec.org/a/eme/caerpp/v3y2011i3p402-411.html
   My bibliography  Save this article

Analysis of meat price volatility in China

Author

Listed:
  • WanChun Luo
  • Rui Liu

Abstract

Purpose - In recent years, frequent volatility is deeply influencing meat industry, household lives and macroeconomics. The main purpose of this paper is to analyze the volatility of Chinese meat price, and provide suggestions on stabilizing the meat market. Design/methodology/approach - This paper uses (G) ARCH, (G) ARCH‐M, TARCH and EGARCH models to analyze volatility and its asymmetry of Chinese meat price. Findings - Estimation result of (G) ARCH model shows volatility clustering of meat price. Estimation result of (G) ARCH‐M model shows high risk and low return in beef market. ARCH and EGARCH models estimation results show non‐symmetry of volatility of beef, mutton and chicken price, and volatility caused by falling price is smaller than that caused by rising price. Originality/value - This paper shows that volatility of meat price can be predicted and Chinese meat market is not perfect, and special attention to the factors causing rise in meat price is necessary.

Suggested Citation

  • WanChun Luo & Rui Liu, 2011. "Analysis of meat price volatility in China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 3(3), pages 402-411, September.
    • Handle: RePEc:eme:caerpp:v:3:y:2011:i:3:p:402-411
    DOI: 10.1108/17561371111165815
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/17561371111165815/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/17561371111165815/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/17561371111165815?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. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Rabemananjara, R & Zakoian, J M, 1993. "Threshold Arch Models and Asymmetries in Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 31-49, Jan.-Marc.
    5. Harris, Milton & Raviv, Artur, 1993. "Differences of Opinion Make a Horse Race," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 473-506.
    6. Hibbert, Ann Marie & Daigler, Robert T. & Dupoyet, Brice, 2008. "A behavioral explanation for the negative asymmetric return-volatility relation," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2254-2266, October.
    7. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Faruk Urak & Abdulbaki Bilgic & Gürkan Bozma & Wojciech J. Florkowski & Erkan Efekan, 2022. "Volatility in Live Calf, Live Sheep, and Feed Wheat Return Markets: A Threat to Food Price Stability in Turkey," Agriculture, MDPI, vol. 12(4), pages 1-24, April.

    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. Hentschel, Ludger, 1995. "All in the family Nesting symmetric and asymmetric GARCH models," Journal of Financial Economics, Elsevier, vol. 39(1), pages 71-104, September.
    2. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    3. Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
    4. Murinde V. & Poshakwala S., 2001. "Volatility in the Emerging Stock Markets in Central and Eastern Europe: Evidence on Croatia, Czech Republic, Hungary, Poland, Russia and Slovakia," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 73-102, July - De.
    5. Duan, Jin-Chuan, 1997. "Augmented GARCH (p,q) process and its diffusion limit," Journal of Econometrics, Elsevier, vol. 79(1), pages 97-127, July.
    6. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    7. P. Kearns & A.R. Pagan, 1993. "Australian Stock Market Volatility: 1875–1987," The Economic Record, The Economic Society of Australia, vol. 69(2), pages 163-178, June.
    8. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    9. Robert F. Engle & Emil N. Siriwardane, 2018. "Structural GARCH: The Volatility-Leverage Connection," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 449-492.
    10. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 9(1), pages 3-21.
    11. A. B. M. Rabiul Alam Beg & Sajid Anwar, 2014. "Detecting volatility persistence in GARCH models in the presence of the leverage effect," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2205-2213, December.
    12. Choi, Jaewon & Richardson, Matthew, 2016. "The volatility of a firm's assets and the leverage effect," Journal of Financial Economics, Elsevier, vol. 121(2), pages 254-277.
    13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    14. Pramod Kumar Naik & Puja Padhi, 2015. "Stock Market Volatility and Equity Trading Volume: Empirical Examination from Brazil, Russia, India and China (BRIC)," Global Business Review, International Management Institute, vol. 16(5_suppl), pages 28-45, October.
    15. Ricardo Alverola, 2007. "Estimating Volatility Returns Using ARCH Models. An Empirical Case: The Spanish Energy Market," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 66, pages 251-276, Enero-Jun.
    16. 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.
    17. Peterburgsky, Stanley, 2021. "Aggregate volatility risk: International evidence," Global Finance Journal, Elsevier, vol. 47(C).
    18. Kui-Wai Li, 2012. "A study on the volatility forecast of the US housing market in the 2008 crisis," Applied Financial Economics, Taylor & Francis Journals, vol. 22(22), pages 1869-1880, November.
    19. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    20. Yung-Shun Chen & Shiu-Tung Wang, 2004. "The empirical evidence of the leverage effect on volatility in international bulk shipping market," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(2), pages 109-124, April.

    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:eme:caerpp:v:3:y:2011:i:3:p:402-411. 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: Emerald Support (email available below). General contact details of provider: .

    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.