Bibliographic citations
Orosco, J., (2019). Uso de los modelos heterocedásticos con Bootstrap en el análisis del Índice General de la Bolsa de Valores de Lima [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/3891
Orosco, J., Uso de los modelos heterocedásticos con Bootstrap en el análisis del Índice General de la Bolsa de Valores de Lima [Tesis]. : Universidad Nacional Agraria La Molina; 2019. https://hdl.handle.net/20.500.12996/3891
@mastersthesis{renati/242961,
title = "Uso de los modelos heterocedásticos con Bootstrap en el análisis del Índice General de la Bolsa de Valores de Lima",
author = "Orosco Gavilán, Juan Carlos",
publisher = "Universidad Nacional Agraria La Molina",
year = "2019"
}
The present research is from applied nature, and it has the objective of analyzing and evaluating the Bootstrap methodology in heterocedastic models, applied in the prediction of the Indice General de la Bolsa de Valores de Lima (IGBVL), period 2010 - 2014. it presents succinctly, the concepts basic temporal series, heteroskedastic serial processes, the Bootstrap methodology and its applications to statistical inference and time series, in this is show the algorithm for heteroscedastic processes GARCH proposed by Pascual et al. (2006) and generalized for models EGARCH and TGARCH. With the procedures shown, predictions were obtained using parametric methodology and Bootstrap methodology, which were compared with real values and finally the performances of both methodologies were evaluated in terms of their prediction. This study obtained that the models that best fit the series are the ARMA(1,1)-GARCH(1,1), ARMA(1,1)-EGARCH(1,1) and ARMA(1,1)- TGARCH(1,1) models each of with the assumption of t-Student distribution with 5 degrees of freedom of the residuals, the comparative analysis showed that the application of the Bootstrap methodology in the series of the returns of Indice General de la Bolsa de Valores de Lima, allow to obtain prediction intervals with greater and equal amplitudes in some forward horizons compared to the parametric methodology, and also allowed to construct with a good performance the prediction intervals for volatilities, thus being an alternative for the construction of prediction intervals in the GARCH, EGARCH and TGARCH models.
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