Un modelo GARCH con asimetría condicional autorregresiva para modelar series de tiempo: Una aplicación para el Indice de Precios y Cotizaciones
[A GARCH model with autorregresive conditional asymmetry to model time-series: An application to the returns of the Mexican Stock Market Index]
AbstractWe develop a GARCH model with autoregressive conditional asymmetry to describe time-series. This means that, in addition to the conditional mean and variance, we assume that the skewness describes the behavior of the time-series. Analytically, we use the methodology proposed by Fernández and Steel (1998) to define the behavior of the innovations of the model. We use the approach developed by Brooks, et. al., (2005), to build it. Moreover, we show its usefulness by modeling the daily returns of the Mexican Stock Market Index (IPC) during the period between January 3rd, 2008 and September 29th, 2009.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 42548.
Date of creation: 07 Nov 2012
Date of revision:
Conditional Asymmetry; GARCH; Skewness; Stock Market Returns; Mexico;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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