<?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>2448-6655</journal-id>
<journal-title><![CDATA[Análisis económico]]></journal-title>
<abbrev-journal-title><![CDATA[Anál. econ.]]></abbrev-journal-title>
<issn>2448-6655</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma Metropolitana, Unidad Azcapotzalco, División de Ciencias Sociales y Humanidades]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-66552022000100143</article-id>
<article-id pub-id-type="doi">10.24275/uam/azc/dcsh/ae/2022v37n94/ramirez</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Hedging Electricity Price Volatility Applying Seasonal and Trend Decomposition]]></article-title>
<article-title xml:lang="es"><![CDATA[Cobertura de Volatilidad del Precio de la Electricidad Aplicando la Descomposición Estacional y de Tendencia]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-García]]></surname>
<given-names><![CDATA[Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saucedo]]></surname>
<given-names><![CDATA[Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico y de Estudios Superiores de Monterrey EGADE Business School ]]></institution>
<addr-line><![CDATA[Mexico City ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico y de Estudios Superiores de Monterrey EGADE Business School ]]></institution>
<addr-line><![CDATA[Mexico City ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>37</volume>
<numero>94</numero>
<fpage>143</fpage>
<lpage>166</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-66552022000100143&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-66552022000100143&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-66552022000100143&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The Wholesale Electricity Market (MEM) has allowed participants to trade electricity at Local Marginal Price (LMP); therefore, developing hedging models to face high volatility electricity prices and avoid financial losses has become essential. This work proposes a methodology based on the Seasonal and Trend Decomposition Model (STL) to the LMP returns series, which is fitted into NIG distribution by obtaining empirical NIG parameters from LMP returns using Maximum Likelihood Estimation (MLE) to generate a simulated NIG distributed series. Finally, the goodness-of-fit test is estimated to demonstrate that empirical data can be fitted into NIG Distribution. This work should be considered the first Electricity Hedging Valuation Methodology for the MEM. Results obtained show that electricity price returns can be fitted and simulated by NIG distribution even through economic crisis periods. The analysis period is from 29/01/2016 to 09/07/2021.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El Mercado Eléctrico Mayorista (MEM) ha permitido a los participantes comercializar electricidad al Precio Marginal Local (LMP) por lo que, se requiere desarrollar modelos de cobertura para enfrentar la alta volatilidad de los precios y así evitar pérdidas financieras. Este trabajo propone una metodología basada en el Modelo de Descomposición Estacional y de Tendencias (STL) aplicado a las series de retornos PML, su ajuste a la distribución NIG obteniendo los parámetros empíricos por Estimación de Máxima Verosimilitud (MLE), para simular una serie distribuida NIG, y por medio de pruebas de bondad de ajuste demostrar el ajuste de los datos a la distribución NIG. Este trabajo debe considerarse la primera Metodología de Valuación de Coberturas Eléctricas para el MEM. Los resultados muestran que los retornos del precio de la electricidad pueden ajustarse a la distribución NIG incluso en períodos de crisis económica. El periodo de estudio contempla del 29/01/2016 al 09/07/2021.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Seasonal and Trend decomposition using Loess (STL)]]></kwd>
<kwd lng="en"><![CDATA[Normal Inverse Gaussian Distribution (NIG)]]></kwd>
<kwd lng="en"><![CDATA[Electricity Price Forecasting (EPF)]]></kwd>
<kwd lng="en"><![CDATA[Wholesale Electricity Market (MEM)]]></kwd>
<kwd lng="en"><![CDATA[Electricity Hedging Valuation]]></kwd>
<kwd lng="en"><![CDATA[C15]]></kwd>
<kwd lng="en"><![CDATA[G10]]></kwd>
<kwd lng="en"><![CDATA[O13]]></kwd>
<kwd lng="en"><![CDATA[P18]]></kwd>
<kwd lng="en"><![CDATA[Q47]]></kwd>
<kwd lng="es"><![CDATA[Descomposición Estacional y de Tendencia usando Loess (STL)]]></kwd>
<kwd lng="es"><![CDATA[Distribución Normal Inversa Gaussiana (NIG)]]></kwd>
<kwd lng="es"><![CDATA[Pronóstico del Precio de la Electricidad (EPF)]]></kwd>
<kwd lng="es"><![CDATA[Wholesale Electricity Market (MEM)]]></kwd>
<kwd lng="es"><![CDATA[Valuación de Cobertura de la Electricidad]]></kwd>
<kwd lng="es"><![CDATA[C15]]></kwd>
<kwd lng="es"><![CDATA[G10]]></kwd>
<kwd lng="es"><![CDATA[O13]]></kwd>
<kwd lng="es"><![CDATA[P18]]></kwd>
<kwd lng="es"><![CDATA[Q47]]></kwd>
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