<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0185-1667</journal-id>
<journal-title><![CDATA[Investigación económica]]></journal-title>
<abbrev-journal-title><![CDATA[Inv. Econ]]></abbrev-journal-title>
<issn>0185-1667</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Economía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0185-16672017000200105</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Pronóstico de precios de petróleo: una comparación entre modelos GARCH y redes neuronales diferenciales]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ortiz Arango]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Panamericana  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>76</volume>
<numero>300</numero>
<fpage>105</fpage>
<lpage>126</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0185-16672017000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0185-16672017000200105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0185-16672017000200105&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: El objetivo del presente trabajo es mostrar las ventajas que tiene el utilizar a las redes neuronales diferenciales (RND) como un método alternativo eficiente en el cálculo de pronósticos de precios futuros de activos financieros, para lo cual se hace un comparativo con modelos de la familia GARCH, al llevar a cabo el pronóstico de precios de cierre de barriles de petróleo crudo de los tipos West Texas International (WTI) y Brent. Los resultados demuestran que el uso de las RND tiene, en esencia, la misma precisión que los valores obtenidos con el modelo TGARCH(1,1) y son superiores a los obtenidos mediante el modelo GARCH(1,1), al calcular los pronósticos de precios de los barriles de petróleo Brent y wti respectivamente durante el periodo de descripción, del 2 de enero de 2013 al 24 de febrero de 2015, y del periodo de pronóstico, del 25 de febrero al 10 de marzo de 2015. Sin embargo, el esfuerzo realizado para obtener tales resultados con la familia de modelos GARCH es significativamente mayor que cuando se utilizan las RND, esto apoya la propuesta de utilizar las RND como un método alternativo fiable en el análisis de series de tiempo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: The aim of this paper is to show the advantages of the use of neural networks differentials (RND) as an efficient alternative method in calculating the forecasts of future prices of financial assets, for which a comparison is made with models of the GARCH family, to carry out the forecast of future closing price of crude oil barrels, types West Texas International and Brent. The results shows that the use of RND has essentially the same accuracy as the values obtained with the TGARCH (1,1) model and are superior to those obtained by the GARCH (1,1) model to calculate price forecasts barrels of crudes Brent and wti respectively during the period of description, from January 2, 2013 to February 24, 2015 and the forecast period from February 25 to March 10, 2015. However, the effort made to obtain such results with the family of garch models is significantly higher than when using the RND, this supports the proposal to use the rnd as a reliable alternative method in the analysis of time series.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[pronósticos de precios]]></kwd>
<kwd lng="es"><![CDATA[GARCH]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales diferenciales]]></kwd>
<kwd lng="en"><![CDATA[Price forecast]]></kwd>
<kwd lng="en"><![CDATA[GARCH]]></kwd>
<kwd lng="en"><![CDATA[differential neural networks]]></kwd>
</kwd-group>
</article-meta>
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