Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru

Abstract: An extensive family of univariate models of autoregressive conditional heteroskedasticity is applied to Peru’s daily stock market returns for the period January 3,1992 to March 30, 2012 with four different specifications related to the distribution of the disturbance term. This c...

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Autor principal:	RODRIGUEZ,GABRIEL
Lenguaje:	English
Publicado:	ILADES. Universidad Alberto Hurtado. 2017
Materias:	Univariate autoregressive conditional heteroskedasticity models Peruvian stock market returns volatility symmetries asymmetries normal t-Student skewed t-Student GED distribution
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-88702017000100069
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spelling	oai:scielo:S0718-887020170001000692017-06-21Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in PeruRODRIGUEZ,GABRIEL Univariate autoregressive conditional heteroskedasticity models Peruvian stock market returns volatility symmetries asymmetries normal t-Student skewed t-Student GED distribution Abstract: An extensive family of univariate models of autoregressive conditional heteroskedasticity is applied to Peru’s daily stock market returns for the period January 3,1992 to March 30, 2012 with four different specifications related to the distribution of the disturbance term. This concerns capturing the asymmetries of the behavior of the volatility, as well as the presence of heavy tails in these time series. Using different statistical tests and different criteria, the results show that: (i) the FIGARCH (1,1)-t is the best model among all symmetric models while the FIEGARCH (1,1)-Sk is selected from the class of asymmetrical models. Also, the model FIAPARCH (1,1)-t is selected from the class of asymmetric power models; (ii) the three models capture well the behavior of the conditional volatility; (iii) however, the empirical distribution of the standardized residuals shows that the behavior of the tails is not well captured by either model; (iv) the three models suggest the presence of long memory with estimates of the fractional parameter close to the region of nonstationarity.info:eu-repo/semantics/openAccessILADES. Universidad Alberto Hurtado.Revista de análisis económico v.32 n.1 20172017-04-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-88702017000100069en10.4067/S0718-88702017000100069
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Univariate autoregressive conditional heteroskedasticity models Peruvian stock market returns volatility symmetries asymmetries normal t-Student skewed t-Student GED distribution
spellingShingle	Univariate autoregressive conditional heteroskedasticity models Peruvian stock market returns volatility symmetries asymmetries normal t-Student skewed t-Student GED distribution RODRIGUEZ,GABRIEL Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
description	Abstract: An extensive family of univariate models of autoregressive conditional heteroskedasticity is applied to Peru’s daily stock market returns for the period January 3,1992 to March 30, 2012 with four different specifications related to the distribution of the disturbance term. This concerns capturing the asymmetries of the behavior of the volatility, as well as the presence of heavy tails in these time series. Using different statistical tests and different criteria, the results show that: (i) the FIGARCH (1,1)-t is the best model among all symmetric models while the FIEGARCH (1,1)-Sk is selected from the class of asymmetrical models. Also, the model FIAPARCH (1,1)-t is selected from the class of asymmetric power models; (ii) the three models capture well the behavior of the conditional volatility; (iii) however, the empirical distribution of the standardized residuals shows that the behavior of the tails is not well captured by either model; (iv) the three models suggest the presence of long memory with estimates of the fractional parameter close to the region of nonstationarity.
author	RODRIGUEZ,GABRIEL
author_facet	RODRIGUEZ,GABRIEL
author_sort	RODRIGUEZ,GABRIEL
title	Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
title_short	Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
title_full	Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
title_fullStr	Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
title_full_unstemmed	Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru
title_sort	selecting between autoregressive conditional heteroskedasticity models: an empirical application to the volatility of stock returns in peru
publisher	ILADES. Universidad Alberto Hurtado.
publishDate	2017
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-88702017000100069
work_keys_str_mv	AT rodriguezgabriel selectingbetweenautoregressiveconditionalheteroskedasticitymodelsanempiricalapplicationtothevolatilityofstockreturnsinperu
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Selecting between autoregressive conditional heteroskedasticity models: An empirical application to the volatility of stock returns in Peru

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