<?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>0120-1751</journal-id>
<journal-title><![CDATA[Revista Colombiana de Estadística]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.Colomb.Estad.]]></abbrev-journal-title>
<issn>0120-1751</issn>
<publisher>
<publisher-name><![CDATA[Departamento de Estadística - Universidad Nacional de Colombia.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-17512010000100003</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Nonparametric Time Series Analysis of the Conditional Mean and Volatility Functions for the COP/USD Exchange Rate Returns]]></article-title>
<article-title xml:lang="es"><![CDATA[Análisis de series de tiempo no paramétrico de las funciones de media y varianza condicional de los retornos de la tasa de cambio COP/USD]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[GALLÓN]]></surname>
<given-names><![CDATA[SANTIAGO]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[GÓMEZ]]></surname>
<given-names><![CDATA[KAROLL]]></given-names>
</name>
<xref ref-type="aff" rid="A02"/>
</contrib>
</contrib-group>
<aff id="A01">
<institution><![CDATA[,Universidad de Antioquia Facultad de Ciencias Económicas Departamento de Estadística y Matemáticas - Departamento de Economía]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="A02">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Ciencias Humanas y Económicas Departamento de Economía]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>15</day>
<month>06</month>
<year>2010</year>
</pub-date>
<pub-date pub-type="epub">
<day>15</day>
<month>06</month>
<year>2010</year>
</pub-date>
<volume>33</volume>
<numero>1</numero>
<fpage>25</fpage>
<lpage>41</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-17512010000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-17512010000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-17512010000100003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[The modeling and estimation of the conditional volatility associated with a stochastic process usually have been based on parametric ARCH-type and stochastic volatility models. These time series models are very powerful in representing the dynamic stochastic properties of the data generating process only if the parametric functions are correctly specified. The nonparametric approach acquires importance as a complementary and flexible method to explore these properties without imposing particular functional forms on the conditional moments of process. This paper presents an application of nonparametric time series methods to estimate the conditional volatility function of the COP/USD exchange rate returns. Additionally, we estimate the conditional mean function under this approach.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[La modelación y estimación de la volatilidad condicional asociada a un proceso estocástico ha estado basada en los modelos paramétricos tipo ARCH y de volatilidad estocástica. Estos modelos son muy poderosos para representar las propiedades dinámicas estocásticas del proceso generador de datos solo si las funciones paramétricas están correctamente especificadas. En este sentido, el enfoque no paramétrico adquiere importancia como un método complementario y flexible para explorar dichas propiedades al no imponer formas funcionales particulares en los momentos condicionales del proceso. Este documento presenta una aplicación de los métodos no paramétricos de series de tiempo para estimar la función de volatilidad condicional de los retornos de la tasa de cambio COP/USD. Además, se estima la función de media condicional bajo este enfoque.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Nonparametric regression]]></kwd>
<kwd lng="en"><![CDATA[Local polynomial regression]]></kwd>
<kwd lng="en"><![CDATA[Nonlinear time series]]></kwd>
<kwd lng="en"><![CDATA[Variance function estimation]]></kwd>
<kwd lng="en"><![CDATA[Autoregressive conditional heteroscedasticity]]></kwd>
<kwd lng="en"><![CDATA[Time series analysis]]></kwd>
<kwd lng="es"><![CDATA[regresión no paramétrica]]></kwd>
<kwd lng="es"><![CDATA[regresión polinomial local]]></kwd>
<kwd lng="es"><![CDATA[series de tiempo no lineales]]></kwd>
<kwd lng="es"><![CDATA[estimación de la función de varianza]]></kwd>
<kwd lng="es"><![CDATA[heterocedasticidad condicional autorregresiva]]></kwd>
<kwd lng="es"><![CDATA[análisis de series de tiempo]]></kwd>
</kwd-group>
</article-meta>
</front><body><![CDATA[  <font size="2" face="verdana">      <p> <b> <font size="4">    <center>Nonparametric Time Series Analysis of the Conditional Mean and Volatility Functions for the COP/USD Exchange Rate Returns</center></font> </b> </p>      <p> <b> <font size="3">     <center> An&aacute;lisis de series de tiempo no param&eacute;trico de las funciones de media y varianza condicional de los retornos de la tasa de cambio COP/USD </center> </font> </b> </p>      <p>     <center> SANTIAGO GALL&Oacute;N<sup>1</sup>,  KAROLL G&Oacute;MEZ<sup>2</sup> </center> </p>      <p> <sup>1</sup>Universidad de Antioquia, Facultad de Ciencias Econ&oacute;micas, Departamento de Estad&iacute;stica y Matem&aacute;ticas - Departamento de Econom&iacute;a, Medell&iacute;n, Colombia. Universidad de Antioquia, Facultad de Ciencias Econ&oacute;micas, Grupo de Econometr&iacute;a Aplicada, Medell&iacute;n, Colombia. Profesor asistente. Email: <a href="mailto:santiagog@udea.edu.co">santiagog@udea.edu.co</a>     <br>  <sup>2</sup>Universidad Nacional de Colombia, Facultad de Ciencias Humanas y Econ&oacute;micas, Departamento de Econom&iacute;a, Medell&iacute;n, Colombia. Universidad de Antioquia, Facultad de Ciencias Econ&oacute;micas, Grupo de Econometr&iacute;a Aplicada, Medell&iacute;n, Colombia. Profesor auxiliar. Email: <a href="mailto:kgomezp@unal.edu.co">kgomezp@unal.edu.co</a>     <br> </p>  <hr size="1">      ]]></body>
<body><![CDATA[<p> <b>     <center> Abstract </center> </b> </p>      <p> The modeling and estimation of the conditional volatility associated with a stochastic process usually have been based on parametric ARCH-type and stochastic volatility models. These time series models are very powerful in representing the dynamic stochastic properties of the data generating process only if the parametric functions are correctly specified. The nonparametric approach acquires importance as a complementary and flexible method to explore these properties without imposing particular functional forms on the conditional moments of process. This paper presents an application of nonparametric time series methods to estimate the conditional volatility function of the COP/USD exchange rate returns. Additionally, we estimate the conditional mean function under this approach. </p>      <p> <b> Key words: </b> Nonparametric regression, Local polynomial regression, Nonlinear time series, Variance function estimation, Autoregressive conditional heteroscedasticity, Time series analysis. </p>  <hr size="1">      <p> <b>     <center> Resumen </center> </b> </p>      <p> La modelaci&oacute;n y estimaci&oacute;n de la volatilidad condicional asociada a un proceso estoc&aacute;stico ha estado basada en los modelos param&eacute;tricos tipo ARCH y de volatilidad estoc&aacute;stica. Estos modelos son muy poderosos para representar las propiedades din&aacute;micas estoc&aacute;sticas del proceso generador de datos solo si las funciones param&eacute;tricas est&aacute;n correctamente especificadas. En este sentido, el enfoque no param&eacute;trico adquiere importancia como un m&eacute;todo complementario y flexible para explorar dichas propiedades al no imponer formas funcionales particulares en los momentos condicionales del proceso. Este documento presenta una aplicaci&oacute;n de los m&eacute;todos no param&eacute;tricos de series de tiempo para estimar la funci&oacute;n de volatilidad condicional de los retornos de la tasa de cambio COP/USD. Adem&aacute;s, se estima la funci&oacute;n de media condicional bajo este enfoque. </p>      <p> <b> Palabras clave: </b> regresi&oacute;n no param&eacute;trica, regresi&oacute;n polinomial local, series de tiempo no lineales, estimaci&oacute;n de la funci&oacute;n de varianza, heterocedasticidad condicional autorregresiva, an&aacute;lisis de series de tiempo. </p>  <hr size="1">      <p> Texto completo disponible en <a href="pdf/rce/v33n1/v33n1a03.pdf">PDF</a> </p>  <hr size="1">      <p> <b> <font size="3"> References </font> </b> </p>       ]]></body>
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Aceptado en null de 2010&#93;</b> </center> <hr size="1">      <p> Este art&iacute;culo se puede citar en <i>LaTeX</i> utilizando la siguiente referencia bibliogr&aacute;fica de <i>BibTeX</i>: </p> <code><font size="2">@ARTICLE{RCEv33n1a03,    <br>  &nbsp;&nbsp;&nbsp; AUTHOR &nbsp;= {Gall&oacute;n, Santiago and G&oacute;mez, Karoll},    <br>  &nbsp;&nbsp;&nbsp; TITLE &nbsp; = {{Nonparametric Time Series Analysis of the Conditional Mean and Volatility Functions for the COP/USD Exchange Rate Returns}},    <br>  &nbsp;&nbsp;&nbsp; JOURNAL = {Revista Colombiana de Estad&iacute;stica},    <br> &nbsp;&nbsp;&nbsp; YEAR &nbsp;&nbsp; = {2010},    <br> &nbsp;&nbsp;&nbsp; volume &nbsp;= {33},    <br> &nbsp;&nbsp;&nbsp; number &nbsp;= {1},    <br> &nbsp;&nbsp;&nbsp; pages &nbsp; = {25-41}    <br> }</font></code>  <hr size="1"> </font>     ]]></body>
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