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Los efectos de largo plazo de la asimetría y persistencia en la predicción de la volatilidad: evidencia para mercados accionarios de América Latina

Author

Listed:
  • Raúl de Jesús Gutiérrez

    (Universidad Autónoma del Estado de México, México)

  • Edgar Ortiz

    (Universidad Nacional Autónoma de México, México)

  • Oswaldo García Salgado

    (Universidad Autónoma del Estado de México, México)

Abstract

Este trabajo propone una extensión al modelo CGARCH a fin de recoger las características de asimetría y persistencia de largo plazo, e investiga sus efectos en el modelado y predicción de la volatilidad condicional de los mercados accionarios de la región de América Latina en el periodo del 2 de enero de 1992 al 31 de diciembre de 2014. En el análisis dentro de la muestra, los resultados estimados de la familia de modelos CGARCH indican la presencia de efectos asimétricos significativos y persistencia de corto y largo plazos en la estructura de la volatilidad de los rendimientos accionarios. Los resultados empíricos también muestran que el uso de medidas simétricas y asimétricas y la prueba estadística de Hansen (2005) son excelentes alternativas para evaluar el poder predictivo de los modelos CGARCH asimétricos. La incorporación de la asimetría y persistencia de largo plazo en la ecuación de la varianza mejora significativamente las predicciones de la volatilidad fuera de la muestra para los mercados accionarios emergentes de Argentina y México.

Suggested Citation

  • Raúl de Jesús Gutiérrez & Edgar Ortiz & Oswaldo García Salgado, 2017. "Los efectos de largo plazo de la asimetría y persistencia en la predicción de la volatilidad: evidencia para mercados accionarios de América Latina," Contaduría y Administración, Accounting and Management, vol. 62(4), pages 1063-1080, Octubre-D.
    • Handle: RePEc:nax:conyad:v:62:y:2017:i:4:p:1063-1080
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    References listed on IDEAS

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    1. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    4. Jian Yang & Cheng Hsiao & Qi Li & Zijun Wang, 2006. "The emerging market crisis and stock market linkages: further evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 727-744, September.
    5. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
    8. Tse, Y. K., 1991. "Stock returns volatility in the Tokyo stock exchange," Japan and the World Economy, Elsevier, vol. 3(3), pages 285-298, November.
    9. Claudio Bonilla & Jean Sepulveda, 2011. "Stock returns in emerging markets and the use of GARCH models," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1321-1325.
    10. 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.
    11. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    12. 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.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Mr. Ferhan Salman & Miss Mali Chivakul & Mr. Ricardo Llaudes, 2010. "The Impact of the Great Recession on Emerging Markets," IMF Working Papers 2010/237, International Monetary Fund.
    15. Bekaert, Geert & Harvey, Campbell R., 2003. "Emerging markets finance," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 3-56, February.
    16. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    17. Khwaja, Asim Ijaz & Mian, Atif, 2005. "Unchecked intermediaries: Price manipulation in an emerging stock market," Journal of Financial Economics, Elsevier, vol. 78(1), pages 203-241, October.
    18. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    19. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    20. Walid, Chkili & Chaker, Aloui & Masood, Omar & Fry, John, 2011. "Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach," Emerging Markets Review, Elsevier, vol. 12(3), pages 272-292, September.
    21. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    22. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    23. Alan E. H. Speight & David G. McMillan, 2004. "Daily volatility forecasts: reassessing the performance of GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 449-460.
    24. Murat Taşdemir & Abdullah Yalama, 2014. "Volatility Spillover Effects in Interregional Equity Markets: Empirical Evidence from Brazil and Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(2), pages 190-202, March.
    25. 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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    More about this item

    Keywords

    Volatilidad asimétrica; Mercados accionarios emergentes; Medidas de errores simétricas y asimétricas; Prueba de poder predictivo superior;
    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
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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