IDEAS home Printed from https://ideas.repec.org/p/ags/iaae15/211556.html
   My bibliography  Save this paper

Asymmetric Volatility Modeling of Spot Prices of Arabic Coffee in Brazil

Author

Listed:
  • da Silva, Carlos Alberto
  • Ferreira, Leo da

Abstract

This paper examines the volatility of daily returns of spot prices of Arabica Coffee through conditional variance models, also called heteroskedasticity models. The data used in the analysis refers to the January 3, 2000 to June 15, 2012 period. The empirical results show reactions of persistency and asymmetry in the variance of their returns, in other words, both good and bad news differently impacts on the volatility of returns according to the EGARCH (1.1) and TARCH (1.1) models. However, from the standpoint of performing predictions, the model that best adapted heteroskedasticity data was EGARCH (1.1) with t - Student's distribution. The coffee market presents strong evidence of such result, since the supply shock yields increases in price levels of the commodity. The empirical results suggest the need of proper strategic instruments of hedging in view of the accentuated shock persistency in Arabic Coffee price volatility returns.

Suggested Citation

  • da Silva, Carlos Alberto & Ferreira, Leo da, 2015. "Asymmetric Volatility Modeling of Spot Prices of Arabic Coffee in Brazil," 2015 Conference, August 9-14, 2015, Milan, Italy 211556, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae15:211556
    DOI: 10.22004/ag.econ.211556
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/211556/files/da%20Silva-Asymmetric%20volatility%20modeling%20of%20spot%20prices%20of%20Arabic%20Coffee-995.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.211556?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
    ---><---

    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. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. 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.
    5. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    6. 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)

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    3. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    4. Bezemer, Dirk & Grydaki, Maria, 2014. "Nonfinancial sectors debt and the U.S. great moderation," Research Report 14030-GEM, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    5. Bezemer, Dirk & Grydaki, Maria, 2013. "Debt and the U.S. Great Moderation," MPRA Paper 47399, University Library of Munich, Germany.
    6. Khalfaoui, R & Boutahar, M, 2012. "Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis," MPRA Paper 41624, University Library of Munich, Germany.
    7. 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.
    8. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
    9. Samih Antoine Azar & Angelic Salha, 2017. "The Bias in the Long Run Relation between the Prices of BRENT and West Texas Intermediate Crude Oils," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 44-54.
    10. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
    11. Marcel Bräutigam & Marie Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," Working Papers hal-02176276, HAL.
    12. Kumari, Jyoti, 2019. "Investor sentiment and stock market liquidity: Evidence from an emerging economy," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 166-180.
    13. Jamal Bouoiyour & Refk Selmi, 2015. "Exchange volatility and export performance in Egypt: New insights from wavelet decomposition and optimal GARCH model," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 24(2), pages 201-227, March.
    14. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.
    15. Eunho Koo & Geonwoo Kim, 2023. "A New Neural Network Approach for Predicting the Volatility of Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1665-1679, April.
    16. Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca, 2022. "Forecasting in GARCH models with polynomially modified innovations," International Journal of Forecasting, Elsevier, vol. 38(1), pages 117-141.
    17. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2024. "The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model," Papers 2404.01641, arXiv.org.
    18. G. Boero & E. Marrocu, 2001. "Evaluating non-linear models on point and interval forecasts: an application with exchange rate returns," Working Paper CRENoS 200110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    19. Gerard H. Kuper & Daan P. van Soest, 2006. "Does Oil Price Uncertainty Affect Energy Use?," The Energy Journal, , vol. 27(1), pages 55-78, January.
    20. Jaleel, Fazeel M. & Samarakoon, Lalith P., 2009. "Stock market liberalization and return volatility: Evidence from the emerging market of Sri Lanka," Journal of Multinational Financial Management, Elsevier, vol. 19(5), pages 409-423, December.

    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:ags:iaae15:211556. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.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.