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Vol. 38 No. 67 (2019)

Economic theory

Testing the Weak Form of Efficient Market Hypothesis and Causality Analysis in Colombian Food Supply Centers

https://doi.org/10.19053/01203053.v38.n67.2019.8040

Published 2019-02-07

  • Leonardo Hernán Talero-Sarmiento
    • ,
  • Henry Lamos-Díaz
    • ,
  • Edwin Alberto Garavito-Hernández

Leonardo Hernán Talero-Sarmiento
Universidad Industrial de Santander

Henry Lamos-Díaz
Universidad Industrial de Santander

Edwin Alberto Garavito-Hernández
Universidad Industrial de Santander

Abstract

In Colombia, there are traditional food supply centers to trade agricultural products. Government institutions publish sale prices of these products in weekly reports. In order to determine the random characteristics of these price series and if there are relationships between the centers, we use a battery of six different tests to examine the weak form of efficient market hypothesis and the Granger causality test. For this, this work collected the historical weekly price of 28 agricultural products, taking into account six markets, during the first week of 2013 to the last week of 2017. The main results indicate that markets tend towards efficiency, however, the efficiency depends on the product traded. In addition, the centers in Manizales, Barranquilla and Villavicencio influence the prices of the Bogotá, Bucaramanga and Medellín markets.

Keywords

Granger causality, Agricultural markets, microeconomics, Agricultural prices

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Author Biography

Leonardo Hernán Talero-Sarmiento

Ingeniero Industrial, Candidato a Magister en Ingeniería Industrial de la Universidad Industrial de Santander. Dirección de correspondencia: Calle 9 # 27, Barrio la Universidad, Universidad Industrial de Santander, Escuela de Estudios Industriales y Empresariales, of. 202. Santander, Colombia. e-mail: leonardo.talero@correo.uis.edu.co

Henry Lamos-Díaz

Doctor en Matemática aplicada de la Universidad Estatal de Moscú. Profesor Titular de la Escuela de Estudios Industriales y Empresarial, Universidad Industrial de Santander.

Edwin Alberto Garavito-Hernández

Magíster en Ingeniería Industrial de la Universidad Mayaguez de Puerto Rico. Profesor Asociado de la Escuela de Estudios Industriales y Empresarial, Universidad Industrial de Santander.

References

  1. Benavides, G. (2009). Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches for futures prices of corn and wheat. Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), 3(2), 40–59.
  2. Bouri, E., Chang, T., & Gupta, R. (2017). Testing the efficiency of the wine market using unit root tests with sharp and smooth breaks. Wine Economics and Policy. https://doi.org/10.1016/j.wep.2017.06.001 DOI: https://doi.org/10.1016/j.wep.2017.06.001
  3. Caeiro, F., & Mateus, A. (2014). randtests: Testing randomness in R. R Package Version, 1.
  4. Coronado Ramírez, S., Celso Arellano, P. L., & Rojas, O. (2015). Adaptive market efficiency of agricultural commodity futures contracts. Contaduria y Administracion, 60(2), 389–401. https://doi.org/10.1016/S0186-1042(15)30006-1 DOI: https://doi.org/10.1016/S0186-1042(15)30006-1
  5. Coronado Ramirez, S., Ramirez Grajeda, M., & Celso Arellano, P. L. (2014). Inefficiency in the international coffee market: The case of Colombian arabica. African Journal of Agricultural Research, 9(5), 556–561. DOI: https://doi.org/10.5897/AJAR11.2228
  6. DeJong, D. N., Nankervis, J. C., Savin, N. E., & Whiteman, C. H. (1992). The power problems of unit root test in time series with autoregressive errors. Journal of Econometrics, 53(1–3), 323–343. https://doi.org/10.1016/0304-4076(92)90090-E DOI: https://doi.org/10.1016/0304-4076(92)90090-E
  7. Departamento Administrativo Nacional de Estadística. (2016). Cuentas Trimestrales- Colombia Producto Interno Bruto (PIB) Boletín Técnico - Cuarto trimestre 2015. Bogotá.
  8. Departamento Administrativo Nacional de Estadística - DANE. (2017). Cuentas trimestrales - Colombia. Producto Interno Bruto (PIB) Boletín Técnico - Cuarto trimestre de 2016. Bogotá: DANE.
  9. Durbin, J., & Watson, G. S. (1951). Testing for Serial Correlation in Least Squares Regression. II. Biometrika, 38(1/2), 159. https://doi.org/10.2307/2332325 DOI: https://doi.org/10.2307/2332325
  10. Fama, E. F. (1965). The Behavior of Stock-Market Prices. Journal of Business, 38(1), 34–105. https://doi.org/10.1017/CBO9781107415324.004 DOI: https://doi.org/10.1086/294743
  11. Geary, R. C. (1935). The ratio the mean deviation to the standard dviation as a test of normality. Biometrika, 27(3–4), 310–332. https://doi.org/10.1093/biomet/27.3-4.310 DOI: https://doi.org/10.1093/biomet/27.3-4.310
  12. Granger, C. W. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics, 48(3), 213–228. https://doi.org/10.1111/j.1468-0084.1986.mp48003002.x DOI: https://doi.org/10.1111/j.1468-0084.1986.mp48003002.x
  13. Gujarati, D. N., & Porter, D. C. (2010). Econometria. McGraw-Hill.
  14. Hassan, A., Abdullah, M. S., & Shah, Z. A. (2007). Testing of random walks and market efficiency in an emerging market: An empirical analysis of Karachi Stock Exchange. The Business Review, Cambridge, 9(1), 271–280.
  15. Karali, B., & Power, G. J. (2013). Short- and long-run determinants of commodity price volatility. American Journal of Agricultural Economics, 95(3), 724–738. https://doi.org/10.1093/ajae/aas122 DOI: https://doi.org/10.1093/ajae/aas122
  16. Lachman, J., & Jack, P. (2017). Study of efficiency and information transmission for agricultural futures markets: A comparative analysis between Buenos Aires and Chicago using monthly and daily data. Estudios Economicos, 34(69), 3–23. DOI: https://doi.org/10.52292/j.estudecon.2017.706
  17. Levy, R. A. (1967). The Theory of Random Walks: A Survey of Findings. The American Economist, 11(2), 34–48. https://doi.org/10.1177/056943456701100205 DOI: https://doi.org/10.1177/056943456701100205
  18. Lo, A. W. (1991). Long-Term Memory in Stock Market Prices. Econometrica, 59(5), 1279–1313. https://doi.org/10.2307/2938368 DOI: https://doi.org/10.2307/2938368
  19. Lo, A. W. (2005). Reconciling Efficient Market with Behavioral Finance: The Adaptative Markets Hypothesis. The Journal of Investment Consulting, 7(2), 1–25. https://doi.org/10.2139/ssrn.728864
  20. Malkiel, B. G. (2003). The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17(1), 59–82. https://doi.org/10.1257/089533003321164958 DOI: https://doi.org/10.1257/089533003321164958
  21. Nisar, S., & Hanif, M. (2012). Testing weak form of efficient market hypothesis: Empirical evidence from South-Asia. World Applied Sciences Journal, 17(4), 414–427. https://doi.org/10.3968/5524
  22. Peters, E. E. (2015). Fractal market analysis : applying chaos theory to investment and economics. Wiley Finance Editions.
  23. Pfaff, B. (2008). Analysis of Integrated Series with R and Cointegrated Time. Use R! DOI: https://doi.org/10.1007/978-0-387-75967-8
  24. Resta, M. (2012). Hurst exponent and its applications in time-series analysis. Recent Patents on Computer Science, 5(3), 211–219. DOI: https://doi.org/10.2174/2213275911205030211
  25. Savin, N., & White, K. (1977). The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica: Journal of the Econometric Society. DOI: https://doi.org/10.2307/1914122
  26. Tansuchat, R., Chang, C., & McAleer, M. (2009). Modelling long memory volatility in agricultural commodity futures returns. Available at SSRN 1491890, 1–34. https://doi.org/10.2139/ssrn.1491890 DOI: https://doi.org/10.2139/ssrn.1491890
  27. Tejeda, H. A., Goodwin, B. K., & others. (2009). Price volatility, nonlinearity, and asymmetric adjustments in corn, soybean, and cattle markets: Implications of ethanol-driven (market) shocks. In 2009 Conference, April 20-21, 2009, St. Louis, Missouri.
  28. Tillman, J. (1975). The power of the Durbin-Watson test. Econometrica, 43(5), 959–974. Retrieved from http://www.jstor.org/stable/1911337 DOI: https://doi.org/10.2307/1911337
  29. Velásquez Henao, J. D., & Aldana Dumar, M. A. (2007). Modelado del precio del café colombiano en la bolsa de Nueva York usando redes neuronales artificiales. Retrieved from http://www.scielo.org.co/pdf/rfnam/v60n2/a16v60n2.pdf
  30. Zeileis, A., & Hothorn, T. (2002). Diagnostic checking in regression relationships.

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