An Empirical Analysis of Price Behavior of Natural Rubber Latex: A Case of Central Rubber Market Hat Yai, Songkhla, Thailand

  • Hari Sharma Neupane
  • Peter Calkins
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 200)


Hat Yai City in Songkhla Province, Thailand has three unique advantages: its Central Rubber Market lies in the largest rubber growing region in the world, it can easily access the new (2004) deep-sea port in Songkhla, and it lies directly on the improved transport infrastructure of the Asia Highway and the North-South Economic Corridor linking it to other growing areas in Southeast Asia and Thailand. Despite these advantages, the rubber industry has always been susceptible to the price volatility of rubber latex, which destabilizes the benefits of rubber production to the local economy, particularly to small-holder producers. Since volatility may theoretically either decrease in the future with the integration of more numerous supplying regions or increase with the intensified co-dependence of supplying and demanding countries, careful modeling of rubber price volatility on the Hat Yai market could both inform development policy today and serve as a baseline for future studies.

This paper therefore attempts to identify the best econometric model to capture price volatility of latex type RSS3 in Thailand for the period 2004-2011. The daily price of latex type RSS3 was modeled by adopting and comparing conditional volatility models, GARCH, GARCH-GJR and EGARCH. The price volatility of natural rubber latex type RSS3 is strongly persistent, and the estimated results are statistically valid. If implemented, the findings of this paper with respect to economic, environmental, and transportation policy could lead to benefits to small holders and to price stabilization mechanisms on national and export.


Natural rubber latex price volatility smallholders livelihood 

JEL codes

C22 N50 


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  1. 1.
    Allen, P.W.: Non-wood products-rubber trees. Encyclopedia of Forest Sciences, 627–633 (2004)Google Scholar
  2. 2.
    Brooks, C.: Introductory econometrics for finance. Cambridge University Press, New York (2008)MATHCrossRefGoogle Scholar
  3. 3.
    Coshall, J.T.: Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management 30(4), 495–511 (2009)CrossRefGoogle Scholar
  4. 4.
    Dean, W.: Brazil and the struggle for rubber: A study in environmental history. Cambridge University Press, UK (1987)Google Scholar
  5. 5.
    Divino, J.A., McAleer, M.: Modeling sustainable international tourism demand to the Brazilian Amazon. Environmental Modeling and Software 24(12), 1411–1419 (2009)CrossRefGoogle Scholar
  6. 6.
    Divino, J.A., McAleer, M.: Modeling and forecasting daily international mass tourism to Peru. Tourism Management 31(6), 846–854 (2010)CrossRefGoogle Scholar
  7. 7.
    Enders, W.: Applied econometric time series. Jhon Wiley and Son Inc., U.K. (2004)Google Scholar
  8. 8.
    Enders, W., Granger, C.W.: Unit-root test and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economic Statistics 16(3), 304–311 (1998)Google Scholar
  9. 9.
    Engle, R.F.: Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4), 987–1007 (1982)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    FAO, Recent developments in natural rubber prices. Consultation on agricultural commodity price problems. Food and Agriculture Organization (FAO), Roam (2002)Google Scholar
  11. 11.
    Gov.Thai PRD: Hat Yai of Songkhla Province designated as rubber city. The Government Public Relation Department (Gov/Thai PRD) (2004), (accessed September 5, 2010)
  12. 12.
    Grilli, E.: The world rubber economy. Johns Hopkins University Press, Baltimore (1980)Google Scholar
  13. 13.
    Huang, B.-W., Chen, M.-G., Chang, C.-L., McAleer, M.: Modeling risk in agriculture finance: Application to the poultry industry in Taiwan. Mathematics and Computers in Simulation 79(5), 1472–1487 (2009)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    IRSG, News Items: Latest RSB, world rubber industry outlook, International Rubber Study Group (IRSG) (2010), (accessed August 15, 2010)
  15. 15.
    Jones, K.P.: Rubber and the environment: Opportunities and constraints for the internalization of environmental costs and benefits into the price of rubber (1997)Google Scholar
  16. 16.
    International Rubber Study Group and the Secretariat of the United Nations Conference on Trade and Development (UNCTAD/IRSG) Workshop: 8–19Google Scholar
  17. 17.
    Kaiyoorawong, S.: Thailand: Rubber prices fluctuate, how can farmers benefit? World Rain Forest Movement (WRM) Bulletin (37), 16–17 (2008)Google Scholar
  18. 18.
    Khin, A., Chong, E.C., Shamsudin, M.N., Mohamed, Z.A.: Natural rubber price forecasting in the world market. In: Agricultural Sustainability Through Participative Global Extension (AGREX 2008), Kuala Lumpur, University of Putra Malaysia (2008)Google Scholar
  19. 19.
    Kittipol, L.: Natural rubber production and outlook of Thailand. Power Point Presentation on Natural Rubber Production and Outlook of Thailand, May 28-29. The Thai Rubber Association, Songhai (2008)Google Scholar
  20. 20.
    Li, W.K., Ling, S., McAleer, M.: Recent theoretical results for time series models with GARCH errors. Journal of Economic Surveys 16(3), 245–269 (2002)CrossRefGoogle Scholar
  21. 21.
    Lim, C., McAleer, M., Min, J.C.: ARMAX modeling of international tourism demand. Mathematics and Computers in Simulation 79(9), 2879–2888 (2009)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Lim, C., McAleer, M.: A seasonal analysis of Asian tourist arrivals to Australia. Applied Economics 32(4), 499–509 (2000); Ling, S., McAleer, M.: Necessary and sufficient moments condition for the GARCH (r,s) and asymmetric power GARCH (r,s) models. Econometric Theory 18(3), 722–729 (2002)Google Scholar
  23. 23.
    Ling, S., McAleer, M.: Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory 19(2), 280–310 (2003)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Ling, S., Li, W.K.: On fractionally integrated autoregressive moving-average models with conditional heteroskedasticity. Journal of the American Statistical Association 9(439), 1184–1194 (1997)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Maddala, G.: Introduction to Econometrics, 2nd edn. MacMillan Publishing Company, New York (1992)Google Scholar
  26. 26.
    McAleer, M., Chan, F., Marinova, D.: An econometric analysis of asymmetric volatility: Theory and application to patents. Journal of Econometrics 139(2), 259–284 (2007)MathSciNetCrossRefGoogle Scholar
  27. 27.
    McAleer, M.: Automated inference and learning in modeling financial volatility. Econometric Theory 21(1), 232–261 (2005)MathSciNetMATHCrossRefGoogle Scholar
  28. 28.
    QMS, User’s Guide Eviews 6 (vol. II). Quantitative Micro Software (QMS), LLC, USA (2007)Google Scholar
  29. 29.
    Saidur, R., Mekhilef, S.: Energy use, energy savings and emission analysis in the Malaysian rubber producing industries. Applied Energy 87(8), 2746–2758 (2010)CrossRefGoogle Scholar
  30. 30.
    Shareef, R., McAleer, M.: Modeling the uncertainty in monthly international tourist arrivals to the Maldives. Tourism Management 28(1), 23–45 (2007)CrossRefGoogle Scholar
  31. 31.
    Somboonsuke, B., Shivakoti, G.P.: Small holders of rubber-based farming systems in Songkhla Province Thailand: Problems and potential solutions. Kasetsart Journal (Soc. Sci.) 22, 79–97 (2001)Google Scholar
  32. 32.
    Somboonsuke, B., Cherdchom, P.: Socio-economic adjustment of smallholding rubber-based farming system: Case study in Southern Region of Thailand. Kasetsart Journal (Soc. Sci.) 21, 158–177 (2000)Google Scholar
  33. 33.
    Stifel, L.D.: The Growth of the Rubber Economy of Southern Thailand. JOSTER: Journal of Southeast Asian Studies 4(1), 107–132 (1973)CrossRefGoogle Scholar
  34. 34.
    Stubbs, R.: Malaysia’s rubber smallholding industry: Crisis and the search for stability. Pacific Affair 56(1), 84–105 (1983)CrossRefGoogle Scholar
  35. 35.
    TERRA, Plantations are not forest, Towards Ecological Recovery and Regional Alliance (TERRA) (Editorial). Watershed: Commercial Tree Plantations in the Mekong Region 9,1–3 (2004)Google Scholar
  36. 36.
    TRA, Para rubber development strategy 2009-2013. The Thai Rubber Association, TRA (2007), (accessed September 15, 2010)
  37. 37.
    UNCTAD, Information on rubber: economic policies. Infocomm: Market information in the commodities area, United Nations Conference on Trade and Development (UNCTAD) (2007), (accessed January 10, 2011)
  38. 38.
    Viswanathan, P.: Emerging smallholder rubber farming systems in India and Thailand: A comparative economic analysis. Asian Journal of Agriculture and Development 5(2), 1–20 (2006)Google Scholar
  39. 39.
    Yang, C.-H., Lin, H.-L., Han, C.-C.: Analysis of international tourist arrivals in China: The role of world heritage sites. Tourism Management 31(6), 827–837 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of EconomicsChiang Mai UniversityChiang MaiThailand
  2. 2.CREATE, Laval UniversityQuebecCanada

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