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Análisis de la volatilidad del índice principal del mercado bursátil mexicano, del índice de riesgo país y de la mezcla mexicana de exportación mediante un modelo GARCH trivariado asimétrico || Volatility Analysis of the Core Mexican Stock Market Index, the Country Risk Index, and the Mexican Oil Basket Using an Asymmetric Trivariate GARCH Model

Author

Listed:
  • Villalba Padilla, Fátima Irina

    (Escuela Superior de Economía. Instituto Politécnico Nacional (México))

  • Flores-Ortega, Miguel

    (Escuela Superior de Economía. Instituto Politécnico Nacional (México))

Abstract

Se parametriza de forma conjunta la heteroscedasticidad condicional autorregresiva generalizada que corresponde al comportamiento de la varianza de tres variables: (a) el índice de precios y cotizaciones (IPC), indicador principal del mercado bursátil mexicano, (b) el emerging markets bond index para México (EMBI), como indicador de riesgo país y (c) el precio de la canasta mexicana de tres crudos de exportación (MEZCLA). Las variables se emplean como estimadores de la tendencia de los precios de las acciones, los bonos y los energéticos, respectivamente, con el objetivo final de conformar un portafolio de inversión diversicado que incluya dichos activos. Se presentan los resultados empíricos de un modelo econométrico GARCH trivariado asimétrico. El modelo permite incorporar la covarianza entre las variables para explicar su interrelación y en la estimación se considera el efecto de los choques generados por las innovaciones positivas y negativas. El estudio contempla el periodo de 2002 a 2013. || We jointly parameterized the generalized autoregressive conditional heteroskedasticity that corresponds to the behavior of the variance of three variables: (a) the core Mexican stock market index (IPC), (b) the Emerging Markets Bond Index for Mexico (EMBI) as country risk pointer and, (c) the Mexican three oil basket exports mix (MEZCLA). The variables are used as trend indicators of stocks, bonds and energetics respectively with the ultimate goal of forming a diversified portfolio including such assets. This paper presents the empirical results of an asymmetric econometric trivariate GARCH model. The model incorporates the covariance between the variables in order to explain their relationship and we considered the shocks generated by positive and negative innovations. The study involves the period 2002- 2013.

Suggested Citation

  • Villalba Padilla, Fátima Irina & Flores-Ortega, Miguel, 2014. "Análisis de la volatilidad del índice principal del mercado bursátil mexicano, del índice de riesgo país y de la mezcla mexicana de exportación mediante un modelo GARCH trivariado asimétrico || Volati," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 3-22, June.
    • Handle: RePEc:pab:rmcpee:v:17:y:2014:i:1:p:3-22
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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    3. Olivier Ledoit & Pedro Santa-Clara & Michael Wolf, 2003. "Flexible Multivariate GARCH Modeling with an Application to International Stock Markets," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 735-747, August.
    4. Helena Chulia & Francisco Climent & Pilar Soriano & Hipolit Torro, 2009. "Volatility transmission patterns and terrorist attacks," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 607-619.
    5. Chris Brooks & Olan T. Henry & Gita Persand, 2002. "The Effect of Asymmetries on Optimal Hedge Ratios," The Journal of Business, University of Chicago Press, vol. 75(2), pages 333-352, April.
    6. Engel, Charles & Rodrigues, Anthony P, 1989. "Tests of International CAPM with Time-Varying Covariances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(2), pages 119-138, April-Jun.
    7. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    8. Valadkhani, Abbas & O'Brien, Martin & Karunanayake, Indika, 2009. "Modelling Australian Stock Market Volatility: A Multivariate GARCH Approach," Economics Working Papers wp09-11, School of Economics, University of Wollongong, NSW, Australia.
    9. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    10. de Goeij, P. C. & Marquering, W., 2004. "Modeling the conditional covariance between stock and bond returns : A multivariate GARCH approach," Other publications TiSEM 94fe5ada-715a-4339-b94c-f, Tilburg University, School of Economics and Management.
    11. 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.
    12. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Guillermo Benavides Perales, 2009. "Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches forfutures prices of corn and wheat," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 3(2), pages 40-59.
    15. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    16. Ng, Angela, 2000. "Volatility spillover effects from Japan and the US to the Pacific-Basin," Journal of International Money and Finance, Elsevier, vol. 19(2), pages 207-233, April.
    17. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    18. Peter de Goeij, 2004. "Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate GARCH Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 531-564.
    19. Stelzer, Robert, 2008. "On The Relation Between The Vec And Bekk Multivariate Garch Models," Econometric Theory, Cambridge University Press, vol. 24(4), pages 1131-1136, August.
    20. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
    21. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
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    More about this item

    Keywords

    volatilidad; rendimiento; asimetría; GARCH trivariado; pronóstico; volatility; return; asymmetry; trivariate GARCH; forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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