<?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>S1665-53462023000100009</article-id>
<article-id pub-id-type="doi">10.21919/remef.v18i1.817</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Detección de periodos de crisis del NASDAQ con EEMD - AE]]></article-title>
<article-title xml:lang="en"><![CDATA[Crisis Periods Detection on NASDAQ Index Via EEMD-AE]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Estrada Sánchez]]></surname>
<given-names><![CDATA[Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Álvarez]]></surname>
<given-names><![CDATA[Federico]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Camacho Ardila]]></surname>
<given-names><![CDATA[Andrés Giovanni]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>18</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1665-53462023000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1665-53462023000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1665-53462023000100009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Se propone identificar el inicio y terminación de las crisis por SARS-CoV-2 y subprime en el NASDAQ. Se utilizó el EEMD para la descomposición del índice en series consecutivas con el mismo número de componentes y se calcularon sus coeficientes de correlación, también se analizó el espectro de potencia de la serie original. Se identificaron señales de inestabilidad asociadas a cambios tanto en las correlaciones de los componentes como del espectro del NASDAQ. Se recomienda aplicar el procedimiento sobre otras series y otras crisis; asimismo, el método se basa en la detección de discrepancias, lo que implica ser una herramienta de monitoreo, mas no una de pronósticos cuantitativos. La originalidad del trabajo radica en el uso del EEMD modificado para la descomposición de series consecutivas en el mismo número de componentes, y la utilización del coeficiente de correlación entre componentes y el espectro de la serie original como medidas de estabilidad del sistema. El enfoque mostró ser útil para identificar y anticipar grandes cambios en el comportamiento de una serie de tiempo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract It is proposed to identify the beginning and end of the SARS-CoV-2 and subprime crises on the NASDAQ. The EEMD was used to decompose the index into consecutive series with the same number of components and their correlation coefficients were calculated, the power spectrum of the original series was also analyzed. Signals of instability associated with changes in both the components&#8217; correlations and the NASDAQ spectrum were identified. It is recommended to apply the procedure on other series and other crises; likewise, the method is based on the detection of discrepancies, thus being a monitoring tool, but not one of quantitative forecasts. The originality of the work lies in the use of the modified EEMD for the decomposition of consecutive series in the same number of components, and the use of the correlation coefficient between components and the spectrum of the original series as measures of system stability. The approach proved to be useful for identifying and anticipating large changes in the behavior of a time series.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[EMD]]></kwd>
<kwd lng="es"><![CDATA[EEMD]]></kwd>
<kwd lng="es"><![CDATA[análisis espectral]]></kwd>
<kwd lng="es"><![CDATA[mercados financieros]]></kwd>
<kwd lng="es"><![CDATA[crisis financieras]]></kwd>
<kwd lng="en"><![CDATA[EMD]]></kwd>
<kwd lng="en"><![CDATA[EEMD]]></kwd>
<kwd lng="en"><![CDATA[spectral analysis]]></kwd>
<kwd lng="en"><![CDATA[financial markets]]></kwd>
<kwd lng="en"><![CDATA[financial crises]]></kwd>
</kwd-group>
</article-meta>
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