<?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>1665-5346</journal-id>
<journal-title><![CDATA[Revista mexicana de economía y finanzas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. econ. finanz]]></abbrev-journal-title>
<issn>1665-5346</issn>
<publisher>
<publisher-name><![CDATA[Instituto Mexicano de Ejecutivos de Finanzas A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1665-53462021000400004</article-id>
<article-id pub-id-type="doi">10.21919/remef.v16i4.490</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Conditional Probability of Jumps in Oil Prices]]></article-title>
<article-title xml:lang="es"><![CDATA[Probabilidad condicional de saltos en precios del petróleo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lorenzo-Valdés]]></surname>
<given-names><![CDATA[Arturo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Popular Autónoma del Estado de Puebla  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>16</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462021000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462021000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462021000400004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The objective of this research is to model the behavior of oil returns. The volatility of oil returns is described through a TGARCH process. Conditional probability jumps are incorporated through uniform, double exponential and normal jump intensity distributions. We found that the volatility of oil returns follows the stylized facts of leptokurtosis, leverage effect and volatility clustering. The abnormal information that causes the jumps, can cause another type of unexpected changes in the following period and the intensity of the jumps has a negative effect on the probability of jumps in the next period. The dynamic model proposed can be extended to other markets and to multivariate time series modeling considering the dependence among the markets&#8217; returns. The main contribution of this work is the estimation of the conditional probability of jumps depending on the previous behavior leading to a better description of the stochastic dynamics of crude oil prices. This will be useful for making better decisions regarding oil as an underlying asset in derivatives or in the formulation of better public policies.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Esta investigación modela el comportamiento de rendimientos del petróleo. La volatilidad de estos rendimientos se describe con un proceso TGARCH. La probabilidad de saltos condicionales se incorpora mediante distribuciones de intensidad de salto uniformes, doble exponencial y normal. Descubrimos que la volatilidad de estos rendimientos sigue los hechos estilizados de leptocurtosis, efecto de apalancamiento y agrupamiento de volatilidad. La información anormal que causa los saltos provoca otro tipo de cambios inesperados en el siguiente período y la intensidad de los saltos tiene un efecto negativo en la probabilidad de saltos en el siguiente período. El modelo dinámico propuesto puede extenderse a otros mercados y a modelos de series de tiempo multivariadas considerando la dependencia entre los rendimientos de los mercados. La principal contribución del trabajo es la estimación de la probabilidad condicional de saltos en función del comportamiento anterior que conduce a una mejor descripción de la dinámica estocástica de los precios del petróleo. Esto será útil para tomar mejores decisiones con respecto al petróleo como activo subyacente en derivados o en la formulación de mejores políticas públicas.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Oil prices]]></kwd>
<kwd lng="en"><![CDATA[TGARCH with jumps]]></kwd>
<kwd lng="en"><![CDATA[Conditional probability]]></kwd>
<kwd lng="es"><![CDATA[Precios del petróleo]]></kwd>
<kwd lng="es"><![CDATA[TGARCH con saltos]]></kwd>
<kwd lng="es"><![CDATA[Probabilidad condicional]]></kwd>
</kwd-group>
</article-meta>
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