Citas bibligráficas
Alva, F., Del, T. (2021). Desarrollo de un modelo simétrico garch para la estimación y predicción de la volatilidad de los retornos del activo de la Caja MunicipaL de Ahorro y crédito DE Maynas, periodo 1998-2020 [Universidad Nacional de la Amazonía Peruana]. https://hdl.handle.net/20.500.12737/7699
Alva, F., Del, T. Desarrollo de un modelo simétrico garch para la estimación y predicción de la volatilidad de los retornos del activo de la Caja MunicipaL de Ahorro y crédito DE Maynas, periodo 1998-2020 []. PE: Universidad Nacional de la Amazonía Peruana; 2021. https://hdl.handle.net/20.500.12737/7699
@misc{renati/967446,
title = "Desarrollo de un modelo simétrico garch para la estimación y predicción de la volatilidad de los retornos del activo de la Caja MunicipaL de Ahorro y crédito DE Maynas, periodo 1998-2020",
author = "Del Águila Paredes, Tulio Ramiro",
publisher = "Universidad Nacional de la Amazonía Peruana",
year = "2021"
}
This research will seek to study the risk behavior of the asset of the CMAC of Maynas during the years 1998 to 2020, analyzing the volatility of the returns of said asset. To this end, in the first instance a solid theoretical framework was established and that supports the theoretical and econometric development of this thesis. According to the methodology, a mean - variance approach was used to estimate and predict the volatility of the returns of such asset. For this reason, the Box - Jenkins methodology was carried out to study the model for the mean from which the ARMA (2,2) was chosen, and the information criteria analysis was used to select the best model for the variance which was the GARCH (1,1). Within this analysis, it was determined that the implication of the lagged conditional variance is much greater than the implication of the lagged shocks squared, this can be validated by the respective estimates of the coefficients of each term. Finally, it was realized that the behavior of the estimated volatility of the asset returns of the Maynas CMAC presented its period of highest levels during the years 1999 to 2007, as well as a validated prediction on the increasing evolution of said volatility. Estimated for the months of July to September of the year 2020.
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