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Modelación de la Volatilidad del Tipo de Cambio del Dólar en el Perú: Aplicación de los Modelos GARCH y EGARCH

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  • Victor Chung Alva

Abstract

Los formuladores de políticas necesitan pronósticos precisos sobre los valores futuros de los tipos de cambio. Esto se debe al hecho de que la volatilidad del tipo de cambio es una medida de incertidumbre útil sobre el entorno económico de un país. El presente estudio tuvo como objetivo determinar el comportamiento volátil del tipo de cambio diario del dólar en el Perú en el periodo del 4 de Enero 2014 al 30 de abril del 2021. El documento revela que la serie de tipo de cambio exhibe regularidades empíricas como volatilidad agrupada, no estacionariedad, no normalidad y correlación serial que justifican la aplicación de la metodología ARCH. Se determinó que existe un comportamiento volátil simétrico que es explicado por el modelo GARCH(1,1). Esto sugiere que el comportamiento del tipo de cambio generalmente está influenciado por información de su comportamiento previo. Esto implica que la volatilidad del tipo de cambio del día anterior puede afectar su volatilidad actual. La principal implicación política de estos resultados es que, dado que la volatilidad del tipo de cambio (riesgo de tipo de cambio) puede aumentar los costos de transacción y reducir las ganancias para el comercio internacional, el conocimiento de la estimación y el pronóstico de la volatilidad del tipo de cambio es importante para la fijación de precios y la gestión de riesgos.

Suggested Citation

  • Victor Chung Alva, 2021. "Modelación de la Volatilidad del Tipo de Cambio del Dólar en el Perú: Aplicación de los Modelos GARCH y EGARCH," Revista de Análisis Económico y Financiero, Universidad de San Martín de Porres, vol. 4(02), pages 07-12.
    • Handle: RePEc:alp:revaef:07-02
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    1. repec:cup:cbooks:9781108422536 is not listed on IDEAS
    2. 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.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    5. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    6. Brooks,Chris, 2019. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781108436823.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

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