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Capacidad de predicción de los modelos GARCH simétricos aplicados a variables financieras de México 2001-2011

Author

Listed:
  • Villalba-Padilla, Fátima Irina

    (Instituto Politécnico Nacional Fecha de recepción: noviembre 2011 - fecha de aceptación: febrero 2012)

  • Flores-Ortega, Miguel

    (Instituto Politécnico Nacional)

Abstract
Este documento presenta los resultados de la evaluación de la capacidad de pronóstico de los modelos garch simétricos aplicados al ipc, el embi, la tasa de fondeo gubernamental, el tipo de cambio fix y la mezcla mexicana de petróleo, como elementos característicos del comportamiento del mercado financiero mexicano y como variables fundamentales para la toma de decisiones de inversión. Se analizan las aportaciones relevantes que incorporan la volatilidad y sus efectos en los procesos de pronóstico que se agrupan en los modelos garch simétricos y se extiende su aplicación a las variables mencionadas./ This document presents the results of the evaluation of the forecast capacity of the symmetric garch models of the following financial variables: ipc, embi, interest rate, exchange rate and Mexican oil mix, as core elements of the economical behavior and basic foundations with regard to investment decisions. This document includes an analysis of the volatility and its effects in forecast processes related to garch models and its application is extended to the above mentioned financial variables.

Suggested Citation

  • Villalba-Padilla, Fátima Irina & Flores-Ortega, Miguel, 2012. "Capacidad de predicción de los modelos GARCH simétricos aplicados a variables financieras de México 2001-2011," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(34), pages 81-124, segundo t.
    • Handle: RePEc:ipn:esecon:v:vii:y:2012:i:34:p:81-124
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    References listed on IDEAS

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    1. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-191, January.
    2. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CARF F-Series CARF-F-189, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Geske, Robert & Roll, Richard, 1983. "The Fiscal and Monetary Linkage between Stock Returns and Inflation," Journal of Finance, American Finance Association, vol. 38(1), pages 1-33, March.
    4. G. Andrew Karolyi, 1992. "Predicting Risk: Some New Generalizations," Management Science, INFORMS, vol. 38(1), pages 57-74, January.
    5. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    6. 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.
    7. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    8. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    9. Mandelbrot, Benoit B, 1972. "Correction of an Error in "The Variation of Certain Speculative Prices" (1963)," The Journal of Business, University of Chicago Press, vol. 45(4), pages 542-543, October.
    10. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    11. Michael McAleer & Marcelo C. Medeiros, 2011. "Forecasting Realized Volatility With Linear And Nonlinear Univariate Models," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 6-18, February.
    12. 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..
    13. 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.
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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