MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01D78607.6CDB8DA0" Este documento es una página web de un solo archivo, también conocido como "archivo de almacenamiento web". Si está viendo este mensaje, su explorador o editor no admite archivos de almacenamiento web. Descargue un explorador que admita este tipo de archivos. ------=_NextPart_01D78607.6CDB8DA0 Content-Location: file:///C:/9E85C06C/802-GALERADA-FINAL.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="windows-1252" Miguel Ángel

 

 

 

 

https://doi.org/10.37815/rt= e.v33n1.802

Artículos originales=

 

Trasmisión de Volatilidad del COVID-19 a los Precios de Acciones del Sector Bancario e= Industrial de Sudamérica, México y Estados Unidos

Transmission of Volatility of COVID-19 to the Share Prices of the Ba= nking and Industrial Sectors of South America, Mexico = and the United States

 

 

Luis Eduardo Peñafiel1<= /span> https://orcid.org/0000-0002-5571-3978

 

1 Pontificia Universidad Católica Argenti= na, Buenos Aires, Argentina

lueduardo1994@gmail.com  

 

Enviado:   =        2021/01/20

Aceptado:  =       2021/04/07

Publicado: =        2021/06/30

                             

Resumen

Este trabajo tiene como objetivo la modelización de la volatilidad de los precios de acciones del sector bancario e industrial de Sudamérica, México y Estados Unidos en el contexto de la pandemia del COVID= -19. Para ello, se estiman los modelos TGARCH (1,1) y EGARCH (1,1). Estos modelos empíricos son ventajosos, porque permiten una respuesta asimétrica de la varianza condicionada en función del signo de los residuos. Es un elemento crucial para los diseños de cartera y gestión de riesgos, y la medición de = la volatilidad de los mercados financieros para capturar potenciales oportunid= ades o pérdidas de inversión. El análisis realizado revela que Brasil fue el más volátil ante el impacto del COVID-19; lo siguen Colombia, Perú, Chile y Argentina. Un caso aparte son Ecuador y México, que no tuvieron impacto significativo en la volatilidad de los precios de las acciones. Por otro la= do, el shock del COVID-19 ha impactado en mayor medida a la volatilidad = del sector industrial en Sudamérica y México comparado con el sector bancario en los Estados Unidos3D"Cuadro.

 

Palabras clave: hetero= cedasticidad condicional, volatilidad, precios, COVID-19.

 

Abstract

This work is aimed at modeling the volatility of share prices in the bankin= g and industrial sectors of South America, Mexico, and the United States during t= he COVID-19 pandemic. For this purpose, the TGARCH (1,1) and EGARCH (1,1) mode= ls are estimated. These empirical models are advantageous because they allow an asymmetric response of the conditioned variance depending on the sign of the residuals. It is a crucial element for portfolio designs and risk managemen= t to measure the volatility of financial markets and capture potential investment opportunities or losses. The analysis carried out reveals that Brazil was t= he most volatile when facing the impact of COVID-19, followed by Colombia, Per= u, Chile, and Argentina. A separate case is Ecuador and Mexico, which did not = have a significant impact on the volatility of share prices. On the other hand, = the COVID-19 shock had a greater impact on the volatility of the industrial sec= tor in South America and Mexico, while affecting greatly the banking sector in = the United States.

 

Keywords: Conditional heteroskedasticity, volatility, pri= ces, COVID-19.

 

      =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =          1.    Introducción

Cada vez má= s los pronósticos indican que los efectos de la pandemia COVID-19 superarán a las crisis financieras pasadas (Peñafiel Chang, et al. 2020; Peñafiel Chang, 20= 21). En principio, el radical confinamiento se ha manifestado en una clara externalidad negativa que ha provocado tres tipos de shock simultáneos. Por= un lado, está el shock de demanda causado por el hecho de que las empre= sas se ven forzadas a realizar reestructuraciones (Peñafiel Chang, 2020a); esto= debido a que, a diferencia de otras crisis, en esta no podrán seguir produciendo c= omo lo hacían antes, a excepción de algunas industrias específicas. =

 =

   Por otro lado, está el shock de ofe= rta que se produce por el periodo de deflación originado por la reducción del g= asto de las familias (Peñafiel Chang, 2020b), y, finalmente, el shock financi= ero, producido por la suma de los dos sh= ocks anteriores, que se caracteriza por el aumento de la morosidad debido a las = dificultades en la devolución de las deudas (Peñafiel Chang, 2019; Peñafiel Chang & = Camelli, 2020). Este estudio se centra en los efectos= sobre los mercados financieros, aunque estos diversos shocks afectan a muchos sectores de la economía de manera disti= nta.

 =

Y es que, c= on la llegada de la pandemia, los mercados financieros de todo el mundo han tenid= o fluctuaciones en los precios de sus activos; fenómeno que se describe como un incremento = de la volatilidad (Chaudhary, Bakhshi, & Gupta, 2020; Chevallier, 2020). De forma = que, tanto para los participantes del mercado financiero como para los regulador= es, es fundamental poseer y entender los indicadores de volatilidad para identi= ficar potenciales oportunidades o pérdidas de inversión. Para ello, esta investig= ación revisa un conjunto de medidas de volatilidad aplicadas a los precios de acciones del sector industrial y bancario de Sudamérica, México y Estados Unidos.

 =

Qué mercados financieros han sido más volátiles en el contexto del COVID-19 es la pregun= ta a la que se trata de responder en esta investigación. Más, precisamente, este trabajo indag= a de modo empírico sobre el rol que ha tenido la pandemia en la volatilidad de l= os mercados financieros de Sudamérica, México y Estados Unidos. Para ello, se estiman los modelos TGARCH (1,1) y EGARCH (1,1); estos modelos empíricos son ventajosos, porque permiten una respuesta asimétrica de la varianza condicionada en función del signo de los residuos.

 =

A parte de = esta primera sección que corresponde a la introducción y que ha explorado a groso modo el estado actual de la problemática planteada, el trabajo está ordenado del siguiente modo: la sección dos revisa la literatura respe= cto con la volatilidad de los mercados financieros; luego, en la sección tres, se presenta un análisis econométrico formal, cuyos resultados se exponen de mo= do resumido en la sección cuatro; para finalizar, se presentan las conclusione= s en la quinta sección.

   &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;        2.    Marco Teórico

La definic= ión de volatilidad en los mercados financieros se asocia a la amplitud de las fluc= tuaciones de los rendimientos de un activo en el tiempo, y aunque se trata de un conc= epto muy intuitivo, no está exento de dificultades (Fernández, 2002). La modelización de la volatilidad asociada a un activo financiero ha crecido exponencialmente y ha dado lugar a un gran volumen de investigaciones  (Bollerslev, Chou, y Kroner, 1992; Chri= stoffersen y Diebold, 1997; Begoña, 1998; Amigo Dobaño, 1999; Claessen y = Mittnik, 2002; Poon y Granger, 2003; Engle, 200= 4; Figlewski, 2004; Frömmel,= 2004; Poon, 2005; Knight y Satchell, 2007; Montenegro, 2010; Opschoor, 2013; DattaChaudhuri y Ghosh, 2015; Rupande, Muguto y Muzindutsi, 2019;  <= /span>Pflueger, Siriwardane y <= span class=3DSpellE>Sunderam, 2019; Bhowmik y= Wang, 2020; Rcolani y Natoli, 2020).

    

La literatura considera a la volatilidad como un proceso que evoluciona de man= era aleatoria en el tiempo. Por ello, la mayoría de los modelos planteados para captar el comportamiento de la volatilidad se formulan sobre alguna medida = de dispersión, cuya desviación típica (= ) es alguna transfor= mación, siendo en esta lo más frecuente =  y = .

 

Las diferentes maneras de representar esta variable surgen como respuesta a la relación existente entre la volatilidad y las variables del conjunto de información. Generalmente, son las novedades sobre su valor fundamental y l= as alteraciones en el entorno macroeconómico las principales fuentes de cambio= s en los precios de los activos (Kim y Wu, 1987; Cooper, Ch= uin y Atkin, 2004; Bilson, Brailsford, y Hooper, 2005; Cauc= hie, Hoesli, y Isakov, 2= 005; Flannery y Protopapadakis, 2005; Baresa, Bogdan, y Ivanovic, 2013; Ato Forson y Janrattanagul, 2014; Bar= nor, 2014; Fama, 2014; Jakpar, = Tinggi, Tak, y Chong, 2018; Demir<= /span>, 2019; Rehman, Kashif, y Sa= leem, 2019).

 

Sin embargo, al existir comúnmente problemas de concordancia entre la frecuenci= a de las variables financieras respecto con las variables macroeconómicas, la op= ción más utilizada por la literatura es la información de la propia historia de = la serie. La evidencia del conjunto de trabajos revisados también muestra que = la capacidad de modelización de la volatilidad depende del mercado analizado y= de las medidas del error de previsión. Por consiguiente, la mejor manera de me= dir la volatilidad vendrá dado por cómo se tenga en cuenta aspectos del mercado= en concreto, la periodicidad de las observaciones y el uso final de la previsi= ón de la volatilidad.

 

En ese sentido, los modelos tipo GARCH han sido fuertemente impulsados al adaptarse con facilidad a las dinámicas antes mencionadas, porque permiten diferentes parametrizaciones de la ecuación de la varianza. Sobre esto exis= ten estudios relevantes (Alonso, 1992; Goyal, 2000; Gazda y Výrost, 2003; Alberg, Shalit, y Yosef, = 2008; Wu, 2010; Panait, 2012; Ali, 2013; Caldeira, Machado, Souza, y Tans= cheit, 2014; Lama, Jha, Paul, y G= urung, 2015; Petrica y Stancu, 2017; Sosa, Bucio, y Cabello, 2019; Hanapi et a= l., 2020; Li, Clements, y Drovandi, 2020).

 

De hecho, diversos trabajos, como los de Bhunia y = Ganguly (2020); Bildirici= , Bayazit, y Ucan (2020); <= span class=3DSpellE>Duttilo, Gattone y Battis= ta (2021); Fakhfekh, Jeribi y = Ben Salem (2021); Lamouchi y B= adkook, 2020; Ngu Chuan Yon= g, Mahdi Ziaei, y R. Szulczyk (2021), han incluido dentro de sus análisis = los modelos tipo GARCH para conocer la volatilidad de los mercados financieros durante el estallido del nuevo coronavirus. Por tal motivo, el interés de e= ste trabajo se centra en los modelos GARCH asimétricos TGARCH y EGARCH, debido a que se requiere la introducción de la posibilidad de que la volatilidad responda de manera asimétrica ante cambios de distintos signos en los preci= os.

 

&nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;  3.    Metodología

En = esta sección se reportan las estimaciones formales de los parámetros de las especificaciones de (φ, = , <= /span>= , <= /span>= ) de los modelos EGARCH y TGARCH. El razonamiento de colección de las variables obedeció al interés de examinar el impacto de la pandemia a la volatilidad de precios de las acciones del sector industrial y bancario. La muestra que se analizó estuvo compuesta por observaciones diarias entre el = 5 de enero hasta el 20 de mayo de 2020, obtenidas de las bolsas de valores de ca= da país.

 

El estudio consideró el análisis de las acciones con mayor presencia bursátil,= es decir, las acciones con mayor grado de liquidez. Se lo calculó dividiendo el número de negociaciones en que la acción ha sido transada en un tiempo determinado para el numero de negociaciones realizadas en total, durante el mismo periodo de tiempo. Para este estudio se consideró las acciones de los siguientes países: Estados Unidos: JPMorgan Chase, Walmart, General Motors; Ecuador: Banco de Guayaquil, Corporación la Favorita, = Natluk; Perú:  Banco BBVA, Empresa Siderúrg= ica del Perú, Unión Andina de Cementos; Colombia:  Bancolombia, Grupo Argos, Grupo Nutresa; Argentina:  Grupo Supervielle, Cablevisión, Aluar Aluminio; Chile: Banco Itaú, Falabella, Embotelladora Andina; México: Grupo Financiero Inbursa, Grupo Bimbo, América móvil; Brasil:  Banco Bradesco, Metalúrgica Gerdau, Atacadao.

 

Con= el fin de analizar el impacto de la pandemia en los mercados financieros, se consideraron los modelos EGARCH y TGARCH, porque son capaces de modelar los cambios asimétricos de los incrementos y decrementos de los rendimientos de= las acciones. Dicho de otro modo, permite captar la volatilidad de manera asimétrica ante cambios de distintos signos en los precios.

 

Un = modelo EGARCH (modelo exponencial generalizado autoregresivo<= /span> condicionalmente heterocedástico), desarrollado inicialmente por Nelson (1991), tiene la virtud de modelar el comportamiento asimétrico de la varianza, que depende del tiempo, y, al mismo momento, garantizar que la varianza sea siempre positiva.

 

Esta técnica modela el efecto de la asimetría al considerar una función g de las innovaciones z_t, que son variables independien= tes e idénticamente distribuidas (i.i.d.) de media ce= ro, que involucra tanto el valor de la innovación z_t como su magnitud expresada por medio de |<= ![if !msEquation]>= | - E(|= <= ![if !msEquation]>= |). Siguiendo la metodología propuesta por Monsegny y Cuervo (20= 08), en términos matemáticos, tenemos:

 

g(= ) =3D (θ + λ= ) =  – λ E(|=                                                 (1)

g(= ) =3D =                                 (<= !--[if supportFields]> SEQ Ecuación \* ARABIC <= span lang=3DES-US style=3D'font-size:11.0pt;line-height:102%;color:windowtext; font-style:normal'>2)

Con= esta definición de g, decimos que un proceso estocástico =  obedece a un modelo en regresión exponen= cial generalizado autoregresivo condicionalmente heterocedástico de órdenes p y q, EGARCH (p, q), si satisface:

 

= |= = <= span style=3D'mso-spacerun:yes'> ~ N ( = ,= = )=                                                 =       (<= span style=3D'mso-no-proof:yes'>3)

=  =3D E[= | =  ]

<= span style=3D'mso-spacerun:yes'>                                                =                      =3D  = <= span style=3D'mso-spacerun:yes'> Ꞵ                                                =                      (4)

Ln (=  =3D Ln VAR[= | =  ]

<= span style=3D'mso-spacerun:yes'>                                    =3D  Ꞓ +  = )                                        <= span class=3DGramE>   (5)

= <= span style=3D'mso-spacerun:yes'> =3D = <= span style=3D'mso-spacerun:yes'> - = <= span style=3D'mso-spacerun:yes'> Ꞵ                                                =                 (6)

Donde:

= : variable aleatoria

T: es un conjunto discreto de índices

= : información pasada

= : vector de observaciones de las variables explicativas

Ꞵ: vector de parámetros desconocidos

Ꞓ: es una constante

= k=3D1∞|<= m:sSubPr><= /span>ψ<= /m:e>k|= 2<= /m:sSup>< ∞= = εt<= ![if !msEquation]>=  =3D <= ![if !msEquation]>=  = , d= onde { <= ![if !msEquation]>= } t ε T = son variables i.i.d de media cero, varianza uno y t= ienen distribución del error generalizado con parámetro (v). El modelo EGARCH y la verosimilitud para T observaciones está dada por:

f( = | = ) =3D =                  =                            (<= span style=3D'mso-element:field-begin'> SEQ Ecuación \* ARABIC 7)

Donde Г (.) es la función gama,

 

= <= span style=3D'mso-spacerun:yes'> =3D  = <= span style=3D'mso-spacerun:yes'>                                                     (8)

 

(v) es un parámetro positivo,= y si es igual a 2, se tiene la distribución normal. A partir del logaritmo de= la verosimilitud para T observaciones, se obtienen las estimaciones de los parámetros para la media y la varianza. La ecuación (5) puede expresarse co= mo:

 

Log ( = ) =3D  φ + =  +  =   =   +  =   log ( = )      =       (9)

( <= ![if !msEquation]>= ) es el logaritmo de la s= erie de la variancia ( <= ![if !msEquation]>= ), que hace que el efecto sea exponencial en vez de cuadrático. Esto asegura que las estimaciones no = sean negativas. Donde, φ =3D es una constante, =  =3D efectos Arch, = =3D efectos asimétricos, = =3D efectos Garch. Si <= ![if !msEquation]>=  =3D <= ![if !msEquation]>=  =3D …=3D 0, el modelo es simétrico. Per= o si <= ![if !msEquation]>=  < 0, implica que shocks negativos generan mayor volatilidad que shocks positivo= s.

Por otro lado, un modelo TGARCH (modelo por umbra= les generalizado autoregresivo, condicionalmente heterocedástico), desarrollado inicialmente por Zakoian (1994), tiene la virtud de dividir la distrib= ución de los shocks en intervalos disjuntos. Dicho esto, un proceso estocástico = , obedece a un modelo en regresi= ón por umbrales generalizado autoregresivo condicionalmente heterocedástico de órdenes p y= q, TGARCH (p, q), si satisface:

= |= = <= span style=3D'mso-spacerun:yes'> ~<= /span> = N ( = ,= = )=                                                      (10) <= /span>

=  =3D E [= | =  ]

                                                =                      =3D  = <= span style=3D'mso-spacerun:yes'> Ꞵ                                                =                         (11)

=  =3D VAR [= | =  ]

                                                                   =3D =  w

                                                =3D φ + =  +  =  =  + =  =       <= /span>   (12)     

S= ea  = <= span style=3D'mso-spacerun:yes'> =3D = <= span style=3D'mso-spacerun:yes'>                            (13)

= <= span style=3D'mso-spacerun:yes'> =3D = <= span style=3D'mso-spacerun:yes'> - = <= span style=3D'mso-spacerun:yes'> Ꞵ                                                =        (14)

Donde:

= : variable aleatoria

T: es un conjunto discreto de índices

= : información pasada

= : vector de variables explicativas observadas en el tiempo t.

= φ: constante

 

La variable =  usa un umbra igual a cero para clasifica= r los impactos pasados. En la ecuación (12), si el coeficiente = , que corresponde al parámetro de apalancamiento, es cero, el modelo se convi= erte en un GARCH (p, q) estándar. De acuerdo con este modelo, cuando se tiene un shock positivo, el efecto que se tiene en la volatilidad es igual a = = . Asimismo, cuando hay un shock negativo, el efecto en la volatilidad es = . A partir del logaritmo de la verosimilitud para T observaciones, se obtienen las estimac= iones de los parámetros para la media y la varianza. La ecuación (12) puede expresarse como:

 

=   =3D φ +  k=3D1pθk ht-k=  +  =  + =  =  ) =                     <= span style=3D'mso-spacerun:yes'>   (15)

= ηi  = son parámetro= s no negativos que satisfacen condiciones similares a los modelos GARCH. Donde: = 𝜃=3D efectos <= span class=3DSpellE>Garch, 𝜂 =3D efectos Arch, 𝜆 =3D efectos asimétricos.

   &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp; 4.    Resultados

I= nminentemente entre el 5 de enero hasta el 20 de mayo de 2020 fue el periodo en que los gobiernos establecieron estrictos confinamientos en toda la región; acontecimiento que constituyó una clara externalidad negativa. En la secció= n de anexos, por una parte, se muestran las estimaciones de los modelos y la evo= lución de los precios (ver Figura= 2) y por otra parte, en la mi= sma sección, Figura= 3, se presentan los rendimien= tos diarios de las acciones y, las pruebas de raíz unitarias (ver Tabla = 9), que garantizan la estacio= nariedad de los datos. En cuanto a la estimación, solo se confirma que el shock de la pandemia afecta la variabilidad de los precios si y solo si ambos modelos (EGARCH (1, 1), TGARCH (1, 1)) indican lo mismo; basta con que uno de ellos= sea no significativo para dar por hecho que el efecto del COVID-19 no afectó a = la volatilidad.

<= o:p> 

A continuación, en el histograma de la Figura 1 se muestra un resumen de las estimaciones de volatilidad de asimétrica (λ) del modelo TGARCH (1,1).=

<= o:p> 

Figura <= /span>1<= /span>

Estimaciones de volatilidad de asimétrica (λ) del modelo = TGARCH (1,1)

Notas. La figura muestra el coeficiente (λ) que es la diferencia de volatilidad en la varianza condicional en condiciones normales con respecto= al shock de la pandemia de los activos financieros. Con fines ilustrativos, se eligieron las acciones que dieron significativos en el modelo TGARCH (1, 1). Fuente: Bolsa de valores de cada país y elaboración propia.

<= o:p> 

P= or último, la elaboración y el diseño de este artículo responde a una minuciosa revisi= ón de literatura que garantizó que la conjetura de investigación se cumpla. Si= endo así, en este estudio se ha logrado construir modelos de la varianza condici= onal para las acciones con mayor presencia bursátil de cada sector de los siguie= ntes países: Estados Unidos: JPMorgan Chase, Walmart, General Motors (ver Tabla = 1); Ecuador: Banco de Guayaqu= il, Corporación la Favorita, Natluk (ver  REF _Ref72592777 \h  \* MERGEFORMAT Tabla = 2)Perú:  Banco BBVA, Empresa Siderúrgica del Per= ú, Unión Andina de Cementos (ver Tabla = 3); Colombia:  Bancolombia, Grupo Argos, Grupo Nutresa= (ver Tabla = 4); Argentina:  Grupo Supervielle, Cablevisión, Aluar Aluminio (ver Tabla = 5); Chile: Banco Itaú, Falabe= lla, Embotelladora Andina (ver Tabla = 6); México: Grupo Financiero = Inbursa, Grupo Bimbo, América móvil (ver Tabla = 7); Brasil:  Banco Bradesco, Metalúrgica Gerdau, Atacadao (ver Tabla = 8).

<= o:p> 

   &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;           5.    Conclusiones

E= n este trabajo se muestra que la volatilidad de los activos financieros siempre ha sido un objeto de estudio de los inversionistas y organismos reguladores pa= ra poder realizar una valoración y gestión del riesgo más eficaz. Además, se analiza la influencia de la pandemia COVID-19 en la volatilidad de acciones= del sector industrial y bancario de Sudamérica, México y Estados Unidos durante= el periodo del 5 de enero de 2020 al 20 de mayo de 2020, mediante la estimació= n de los modelos EGARCH (1,1) y TGARCH (1,1).

<= o:p> 

L= os hallazgos de este trabajo resultan interesantes en cuanto se evidencian cin= co hechos estilizados para el mercado accionario de Sudamérica, México y Estad= os Unidos. Los resultados obtenidos muestran que: i) la pandemia no afectó significativamente a la volatilidad de las acciones de Ecuador y México; ii) la pandemia si afectó a la variabilidad de los pr= ecios de las acciones de: Chile (Banco Itau), Argenti= na (Cablevisión), Colombia (Bancolombia y Grupo Nutresa), Perú ( Unión Andina = de Cementos y Banco BBVA), Brasil ( Banco Bradesco, Metalúrgica Gerdau y Atacadao), Estados Unidos ( JPMorgan Chase, Walmart, General Motors); iii) la volatilidad de Brasil = es superior al resto de países; iv)  a diferencia de Estados Unidos, en los = demás países, en general, el shock de la pandemia afecta en mayor medida al sector industrial; v) el impacto de la pandemia en la volatilidad de los precios de las acciones de la región es bastante heterogénea, es decir, mientras que en Estados Unidos la pandemia afecta a la volatilidad de los precios de todas = las empresas, para el caso de la región no aplica lo mismo.

<= span style=3D'mso-spacerun:yes'>   

S= ería interesante en futuros estudios extender el análisis a un mayor número de acciones y otros tipos de activos financieros. Por otra parte, los resultad= os inevitablemente estarán sesgados por el criterio técnico del profesional, l= as decisiones impactarán en la elección de los métodos que capten el comportamiento de la volatilidad. En todo caso, la información obtenida de = este estudio procura mostrar los efectos y las consecuencias de una pandemia a n= ivel global dentro del plan de acción de la política monetaria y administración = de riesgos de los mercados de capitales.

<= o:p> 

Referencias=

Alberg, D., Shalit, H., & Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201–1208. https://doi.org/10.1080/09603100701604225

Ali, G. (2013). EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Mo= dels for Pathogens at Marine Recreational Sites. Journal of Statistical and Econometric Methods, 2(3), 1–6.<= /span>

Alonso, F. (1992). La Volati= lidad Del Mercado De Acciones Español. Retrieved from https://core.ac.uk/download/pdf/6501420.pdf

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 =

Anexos

 

Tabla 1

Estimaciones de los parámetros y pruebas de diagnóstico para Estados Unidos

&n= bsp;

JPMorgan Chase

EGARCH (1,1)

JPMorgan Chase

TGARCH (1,1)

Walmart=

EGARCH (1,1)

Walmart=

TGARCH (1,1)

General Motors

EGARCH (1,1)

General Motors

TGARCH (1,1)

c

-0,0039

-0,0052

-0,0023

-0,0019

-0,0056

-0,0034

 

[0,0782]

[0,0006]

[0,1037]

[0,1759]

[0,0514]

[0,1900]

Var (-1)

-0,1663

-0,1709

-0,1205

-0,1574

0,1520

0,1538

 

[0,1002]

[0,0210]

[0,1998]

[0,0762]

[0,1577]

[0,1770]

φ

-0,4322

0,0000

-0,4982

0,0000

-0,3533

0,0000

 

[0,0274]

[0,0009]

[0,0211]

[0,3755]

[0,0325]

[0,3802]

η

0,1154

-0,2516

0,3408

0,0474

0,1220

-0,0001

 

[0,6448]

[0,1040]

[0,0276]

[0,6468]

[0,5946]

[0,9993]

λ

-0,3855

0,6993

-0,2347

0,4171

-0,3238

0,4007

 

[0,0032]

[0,0001]

[0,0270]

[0,0221]

[0,0177]

[0,0274]

θ

0,9520

0,9235

0,9652

0,8062

0,9559

0,8385

 

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

0,0960

0,0993

0,0473

0,0625

-0,0313

-0,0293

Akaike

-4,1557

-4,2272

-5,0416

-5,057

-3,6943

-3,6568

Schwarz

-3,9954

-4,0669

-4,8814

-4,8969

-3,5337

-3,4963

Hannan-Quinn

-4,0909

-4,1623

-4,0769

-4,9924

-3,6292

-3,5920


Notas. Esta tabla muestra los resultados de regresión que provienen de la estimación de las ecuaciones (9) y (15), siendo el p-v= alue los valores que están en corchetes. La interpretación del parámetro de asimetría en los modelos EGARCH (1,1) indican que si se satisface que λ < 0 implica que un shock negativo genera mayor volatilidad que un shock positivo. Mientras que la interpretación del parámetro de asimetría en los modelos TGARCH (1,1) indican que si se satisface que λ > 0 implica = que para ese activo hay asimetría en las noticias (Shock), es decir, que un sho= ck negativo tiene mayores efectos de volatilidad que un shock positivo. Los da= tos de las acciones provienen de la bolsa de valores de Estados Unidos. Elabora= ción propia. 

 

Para el caso de Estados Unidos, las estimaciones del parámetro (_= 5;) fueron significativas e indican que la pandemia afectó a la variabilidad de= los precios de las acciones JPMorgan Chase, Walmart y General Motors. La estima= ción de la varianza condicional frente al shock de la pandemia y en condiciones normales queda construida como:

 

=  = =3D   0,000 + 0,9235=  + ( -0,2561 + 0,6993)  =                  (16)

=  JPMorgan =3D   0,000 + 0,9235=  = + ( -0,2561)  =                               (17)

 

=  =  = =3D   0,000 + 0,8062=  + (0,0474 + 0,4171)  =         =           (18)

=  Walmart =3D   0,000 + 0,8062=  + (0,0474)  =                                   (19)

 

=  =  = =3D   0,000 + 0,8385=  = + ( -0,0001 + 0,4007)  =          (20)

=  General Motors =3D   0,000 + 0,8385=  = + ( -0,0001)  =                          (21)

 

 

 

 

 

 

 

Tabla 2

Estimaciones de los parámetros y pruebas de diagnóstico para Ecuador

 

Banco de Guayaquil

EGARCH (1,1)

Banco de Guayaquil TGARCH (1,1)

Corp. La Favorita

EGARCH (1,1)

Corp. La Favorita

TGARCH (1,1)

Natluk

 

EGARCH (1,1)

Natluk

 

TGARCH (1,1)

C

-0,0032

-0,0051

-0,0001

-0,0000

0,0017

0,0002

 

[0,0000]

[0,1196]

[0,7199]

[0,7923]

[0,0000]

[0,9056]

Var (-1)

-0,4490

-0,4148

-0,2501

-0,2655

0,2950

-0,0763

 

[0,0000]

[0,0022]

[0,0530]

[0,0420]

[0,0340]

[0,6207]

φ

-1,3163

0,0001

-3,6063

0,0000

-7,4719

0,0000

 

[0,0026]

[0,2407]

[0,0625]

[0,1706]

[0,0000]

 [0,000]

η

-0,4747

-0,0102

0,4602

0,0841

-2,2465

-0,0345

 

[0,0002]

[0,9374]

[0,0203]

[0,5739]

[0,0000]

[0,0000]

λ

-0,5204

0,2913

-0,2203

0,4904

2,7465

0,6629

 

[0,0000]

[0,4815]

[0,1965]

[0,1953]

[0,0000]

[0,0000]

θ

0,7918

0,6567

0,7135

0,4731

0,1304

0,9477

 

[0,0000]

[0,0191]

[0,0000]

[0,0947]

[0,4568]

[0,0000]

0,1440

0,1564

0,0659

0,0668

-0,1424

-0,0088

Akaike

-4,9527

-4,7418

-8,5268

-8,5289

-6,9777

-7,7145

Schwarz

-4,7553

-4,5444

-8,3675

-8,3696

-6,7461

-7,4829

Hannan-Quinn

-4,8746

-4,6637

-8,4624

-8,4645

-6,8898

-7,6266

Notas. Esta ta= bla muestra los resultados de regresión que provienen de la estimación de las ecuaciones (9) y (15), siendo el p-value los va= lores que están en corchetes. La interpretación del parámetro de asimetría en los modelos EGARCH (1,1) indican que si se satisface que λ < 0 implica = que un shock negativo genera mayor volatilidad que un shock positivo. Mientras = que la interpretación del parámetro de asimetría en los modelos TGARCH (1,1) indican que si se satisface que λ > 0 implica que para ese activo h= ay asimetría en las noticias (Shock), es decir, que un shock negativo tiene mayores efectos de volatilidad que un shock positivo. Los datos de las acci= ones provienen de la bolsa de valores de Ecuador. Elaboración propia.<= span style=3D'mso-bidi-font-size:11.0pt;mso-fareast-font-family:Calibri;mso-fare= ast-language: EN-US'>

&nb= sp;

Para el = caso de Ecuador, las estimaciones del parámetro (λ) fueron no significativa= s en los siguientes modelos:  i) TGARCH = del Banco Guayaquil; ii) EGARCH y TGARCH de Corpora= ción la Favorita iii) EGARCH de Natluk debido a que no se satisface la condición de λ < 0.  Por lo tanto, se muestra que la pandemi= a no afectó a la variabilidad de los precios de estas acciones. La estimación de= la varianza condicional en condiciones normales queda construida como:

 

=  Banco de Guayaquil =3D   0,0001 + 0,6567=  + ( -0,0102)  =                (22)

=  Corp. La Favorita =3D   0,0000 + 0,4731=  + (0,0841)  =                    (23)

=  = Natluk =3D   0,0000 + 0,9477=  = + ( -0,0345)  =                            (24)

 <= /o:p>

 

 

 

 

 

 

 

 

 

Tabla 3

Estimaciones de los parámetros y pruebas de diagnóstico para Perú

 

Banco BBVA

EGARCH (1,1)

Banco BBVA

TGARCH (1,1)

Empresa Siderúrgica  =

EGARCH (1,1)

Empresa Siderúrgica  =

TGARCH (1,1)

U. Andina

Cementos

EGARCH (1,1)

U. Andina

Cementos

TGARCH (1,1)

c

-0,0039

-0,0021

-0,0017

-0,0024

-0,0037

-0,0016

 

[0,0000]

[0,0814]

[0,6383]

[0,0257]

[0,0000]

[0,0695]

Var (-1)

-0,1414

0,0014

0,0788

0,0619

0,2127

0,0295

 

[0,1489]

[0,9929]

[0,4587]

[0,4735]

[0,0112]

[0,8696]

φ

-0,8259

0,0000

-4,1861

-0,0000

-0,4794

0,0000

 

[0,0000]

[0,0000]

[0,4586]

[0,0451]

[0,0002]

[0,1336]

η

-0,2240

-0,0786

-0,0862

0,0231

-0,3535

-0,0174

 

[0,0114]

[0,0000]

[0,6576]

[0,0000]

[0,0185]

[0,7296]

λ

-0,5114

0,4958

-0,1382

-0,0637

-0,4218

0,8626

 

[0,0000]

[0,0000]

[0,5955]

[0,0000]

[0,0004]

[0,0014]

θ

0,8778

0,7153

0,4186

1,0591

0,9063

0,7520

 

[0,0000]

[0,0000]

[0,5841]

[0,0000]

[0,0000]

[0,0000]

0,0198

-0,0092

0,0074

0,0071

0,0200

-0,0422

Akaike

-5,4392

-5,3338

-4,3227

-4,5147

-5,5054

-5,2234

Schwarz

-5,2631

-5,1623

-4,1466

-4,3386

-5,3293

-5,0473

Hannan-Quinn

-5,3685

-5,2677

-4,2523

-4,4434

-5,4347

-5,1527

Notas.

                   

Para el = caso del Perú, las estimaciones del parámetro (λ) fueron no significativas = en el siguiente modelo:  i) EGARCH de = la empresa Siderúrgica del Perú.  Por = lo tanto, se muestra que la pandemia afectó a la variabilidad de los precios de las acciones Banco BBVA y Unión Andina de cementos. La estimación de la varianza condicional frente al shock de la pandemia y en condiciones normal= es queda construida como:

=  = =3D   0,000 + 0,7153=  + ( -0,0786 + 0,4958)  =            (25)

=  Banco BBVA =3D   0,000 + 0,7153=  + ( -0,0786)  =                              (26)

 

=  Emp. Siderúrgica  =3D   -0,000 + 1,0591=  + (0,0231)  =                        (27)

 

=  =  =3D   0,000 + 0,7520=  + ( -0,0174 + 0,8626)  =          (28)

=  <= span style=3D'font-size:11.0pt;mso-bidi-font-size:12.0pt;font-family:"Times New = Roman",serif; mso-fareast-font-family:"Times New Roman";position:relative;top:2.5pt; mso-text-raise:-2.5pt;mso-ansi-language:ES-EC;mso-fareast-language:ES-TRAD; mso-bidi-language:AR-SA'>=  =3D   0,000 + 0,7520=  + ( -0,0174)  =                   (29)

 = ;

 = ;

 = ;

 = ;

 = ;

 = ;

 = ;

 = ;

Tabla 4

Estimaciones de los parámetros y pruebas de diagnóstico para Colombia<= /p>

 

Bancolombia

EGARCH (1,1)

Bancolombia

 TGARCH (1,1)

Grupo Argos 

EGARCH (1,1)

Grupo Argos 

TGARCH (1,1)

Grupo Nutresa

EGARCH (1,1)

Grupo Nutresa

TGARCH (1,1)

c

-0,0071

-0,0051

-0,0065

-0,0069

-0,0248

-0,0027

 

[0,0013]

[0,0000]

[0,0103]

[0,0365]

[0,0003]

[0,0069]

Var (-1)

0,1139

-0,0378

0,0935

0,2097

-0,2301

-0,1101

 

[0,3491]

[0,7811]

[0,4305]

[0,1065]

[0,0043]

[0,2086]

φ

-0,3524

0,0000

-0,6830

0,0000

-0,1179

0,0000

 

[0,0987]

[0,0000]

[0,0012]

[0,1267]

[0,3731]

[0,0000]

η

-0,2864

-0,2448

0,4728

0,2015

-0,1916

-0,1538

 

[0,0082]

[0,0000]

[0,0005]

[0,1683]

[0,1239]

[0,0031]

λ

-0,4404

0,3966

-0,1429

0,2770

-0,2565

0,2818

 

[0,0001]

[0,0000]

[0,1946]

[0,3261]

[0,0001]

[0,0000]

θ

0,9118

1,0326

0,9472

0,6823

0,9616

1,0043

 

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

0,0161

-0,00142

0,0404

0,0673

-0,0971

-0,0349

Akaike

-4,1684

-4,1300

-3,5924

-3,5443

-5,0996

-5,0012

Schwarz

-4,0061

-3,9677

-3,4301

-3,3820

-4,9373

-4,8389

Hannan-Quinn

-4,1028

-4,0644

-3,5368

-3,4788

-5,0340

-4,0367

Notas.

 

Para el = caso de Colombia, las estimaciones del parámetro (λ) fueron no significativ= as en los siguientes modelos:  i) EGAR= CH y TGACRH del Grupos Argos.  Por lo ta= nto, se muestra que la pandemia afectó a la variabilidad de los precios de las acci= ones Bancolombia y Grupo Nutresa. La estimación de la varianza condicional frent= e al shock de la pandemia y en condiciones normales queda construida como:<= /o:p>

=  = =3D   0,0000 + 1,0326=  + ( -0,2448 + 0,3966)  =              (30)

=  Bancolombia =3D   0,000 + 1,0326=  + ( -0,2448)  =                                   (31)=

 

=  Grupo Argos =3D   0,0000 + 0,68231=  + (0,2015)  =                               (32)

 

=  =  =3D   0,0000 + 1,0043=  + ( -0,1538 + 0,2818)  =             (33)

=  =  =3D   0,0000 + 1,0043=  + ( -0,1538)  =                            (34)

 

 

 

 

 

 

 

 

 

Tabla 5

Estimaciones de los parámetros y pruebas de diagnóstico para Argentina=

 

Grupo Supervielle

EGARCH (1,1)

Grupo Supervielle

TGARCH (1,1)

Cablevisión 

 

EGARCH (1,1)

Cablevisión 

 

TGARCH (1,1)

Aluar Aluminio

EGARCH (1,1)

Aluar Aluminio

TGARCH (1,1)

c

0,0002

0,0008

-0,0006

-0,0020

-0,0020

0,0003

 

[0,9695]

[0,8834]

[0,8776]

[0,3081]

[0,0621]

[0,9443]

Var (-1)

0,0549

0,0940

0,0666

0,1088

0,0044

0,0429

 

[0,7597]

[0,5712]

[0,6061]

[0,2572]

[0,9605]

[0,7228]

φ

-0,9550

0,0006

-0,5548

0,0000

-0,0585

0,0001

 

[0,2801]

[0,1298]

[0,0936]

[0,0366]

[0,0000]

[0,1998]

η

0,2558

-0,0295

0,1723

-0,1434

-0,1120

0,0004

 

[0,1758]

[0,6964]

[0,1298]

[0,0057]

[0,0000]

[0,9949]

λ

-0,1657

0,3242

-0,1950

0,3148

-0,1740

0,2849

 

[0,0433]

[0,1042]

[0,0356]

[0,0024]

[0,0000]

[0,0903]

θ

0,8725

0,6518

0,9317

0,9988

0,9749

0,7506

 

[0,0000]

[0,0029]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

-0,0091

-0,0194

0,0034

0,0018

-0,0024

-0,0002

Akaike

-2,9807

-2,9870

-3,4842

-3,4980

-3,5587

-3,4843

Schwarz

-2,8152

-2,8214

-3,3187

-3,3325

-3,3932

-3,3884

Hannan-Quinn

-2,9139

-2,9201

-3,4174

-3,4312

-3,4919

-3,4176

Notas. (9) y (15), siendo el p-va= lue los valores que están en corchetes. La interpretación del parámetro de asimetría en los modelos EGARCH (1,1) indican que si se satisface que λ < 0 implica que un shock negativo genera mayor volatilidad que un shock positivo. Mientras que la interpretación del parámetro de asimetría en los modelos TGARCH (1,1) indican que si se satisface que λ > 0 implica = que para ese activo hay asimetría en las noticias (Shock), es decir, que un sho= ck negativo tiene mayores efectos de volatilidad que un shock positivo. Los da= tos de las acciones provienen de la bolsa de valores Argentina. Elaboración pro= pia.

 

Para el = caso de Argentina, las estimaciones del parámetro (λ) fueron no significati= vas en los siguientes modelos:  i) TGAR= CH del Grupo Superville; ii) TGARCH de Aluar Aluminio.  Por lo t= anto, se muestra que la pandemia afectó a la variabilidad del precio de la acción= de Cablevisión. La estimación de la varianza condicional frente al shock de la pandemia y en condiciones normales queda construida como:=

 

=  Grupo Supervielle =3D   0,0006 + 0,6518=  + ( -0,0295)  =                          (35)

 

=  =  =3D   0,0000 + 0,9988=  + ( -0,1434 + 0,3148)  =               (36)

=  Cablevisión =3D   0,0000 + 0,99881=  + ( -0,1434)  =                                  (37)

 

=  =  =3D   0,0001 + 0,7506=  + ( 0,0004 )  =                          (38)<= /span>

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tabla 6

Estimaciones de los parámetros y pruebas de diagnóstico para Chile

 

Itau Corp Bank

EGARCH (1,1)

Itau Corp Bank

TGARCH (1,1)

Falabella 

 

EGARCH (1,1)

Falabella 

 

TGARCH (1,1)

Embotelladora

Andina

EGARCH (1,1)

Embotelladora

Andina

TGARCH (1,1)

c

-0,0125

-0,0107

-0,0052

-0,0027

-0,0018

-0,0030

 

[0,0000]

[0,0002]

[0,2373]

[0,1211]

[0,6096]

0,1502

Var (-1)

-0,1370

-0,2509

0,0717

-0,1029

-0,1947

-0,2182

 

[0,2303]

[0,0553]

[0,1121]

[0,4393]

[0,1704]

[0,0824]

φ

-0,3054

0,0000

-8,2012

0,0000

-8,0725

0,0000

 

[0,1051]

[0,0474]

[0,0000]

[0,7212]

[0,0011]

[0,4123]

η

-0,0943

-0,1231

-0,5035

0,1761

0,5578

0,1365

 

[0,4619]

[0,1933]

[0,0584]

[0,1555]

[0,0326]

[0,3198]

λ

-0,4336

0,5452

0,5327

0,6497

0,4242

0,4622

 

[0,0000]

[0,0003]

[0,0044]

[0,0231]

[0,0113]

[0,0211]

θ

0,9394

0,83331

-0,2868

0,7151

-0,1110

0,7377

 

[0,0000]

[0,0000]

[0,0549]

[0,0000]

[0,7555]

[0,0000]

0,0716

0,1079

0,0001

-0,0220

0,1164

0,1235

Akaike

-4,1417

-4,0447

-3,6334

-3,9527

-3,9471

-4,2944

Schwarz

-3,9814

-3,8844

-3,4731

-3,7924

-3,7866

-4,1341

Hannan-Quinn

-4,0769

-3,9799

-3,5686

-3,8879

-3,8823

-4,2296

Notas.

 

Para el = caso de Chile, las estimaciones del parámetro (λ) fueron no significativas = en los siguientes modelos:  i) EGARCH = de Falabella debido a que no se satisface la condición de λ < 0; ii) EGARCH de Embotelladora Andina debido a que tampo= co satisface la condición de λ < 0.  Por lo tanto, se muestra que la pandemia afectó solo a la variabilid= ad del precio de la acción de Itaú Corp Bank. La estimación de la varianza condicional frente al shock de la pandemia y en condiciones normales queda construida como:

 

=  =  =3D   0,0000 + 0,8331=  + ( -0,1231 + 0,5452)  =           (39)

=  =  =3D   0,0000 + 0,8331=  + ( -0,1231)  =                                (40)

 

=  Falabella =3D   0,0000 + 0,7151=  + (0,1761)  =                                    (41)

 

=  =  =3D   0,0000 + 0,7377=  + (0,1365)  =                       (42)

 

 

 

 

 

 

 

 

 

Tabla = 7

Estimaciones de los parámetros y pruebas de diagnóstico para México

 

G. F. Inbursa

EGARCH (1,1)

G. F. Inbursa

 TGARCH (1,1)

Grupo Bimbo

EGARCH (1,1)

Grupo Bimbo 

TGARCH (1,1)

América

Móvil

EGARCH (1,1)

América

Móvil

TGARCH (1,1)

c

-0,0079

-0,0072

-0,0021

-0,0023

-0,0020

-0,0030

 

[0,0022]

[0,0146]

[0,4287]

[0,4024]

[0,4983]

[0,0000]

Var (-1)

-0,0911

-0,0964

-0,1190

-0,1298

-0,0691

-0,0574

 

[0,4348]

[0,4515]

[0,3294]

[0,2929]

[0,5426]

[0,6248]

φ

-1,5282

0,0001

-0,3843

0,0000

-0,6054

0,0000

 

[0,1715]

[0,2618]

[0,0688]

[0,3197]

[0,0654]

[0,0000]

η

0,6127

0,2489

0,2432

0,0318

0,0760

-0,1127

 

[0,0593]

[0,3641]

[0,0271]

[0,5636]

[0,6088]

[0,0000]

λ

-0,2450

0,4624

-0,0955

0,2113

-0,1202

0,0690

 

[0,1544]

[0,3255]

[0,2511]

[0,0746]

[0,3296]

[0,0723]

θ

0,8518

0,4213

0,9696

0,8814

0,9227

1,0600

 

[0,0000]

[0,1449]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

0,0053

0,0061

0,0178

0,0179

0,0079

0,0013

Akaike

-4,0738

-4,0646

-4,0682

-4,0484

-4,5065

-4,5646

Schwarz

-3,9114

-3,9022

-3,9059

-3,8860

-4,3442

-4,4022

Hannan-Quinn

-4,0824

-3,9990

-4,0026

-3,9828

-4,4408

-4,4990

Notas.

 

Para el = caso de México, las estimaciones del parámetro (λ) fueron no significativas= en todos los modelos.  Por lo tanto, se muestra que la pandemia no afectó a la variabilidad de los precios de las acciones de: Grupo Financiero Inbursa, Grupo Bimbo y América móvil. La estimación de la varianza condicional en condiciones normales queda constru= ida como:

 

=  =  =3D   0,0001 + 0,4213=  + (0,2489)  =                      (43)

 

=  Grupo Bimbo =3D   0,0000 + 0,8814=  + (0,0318)  =                       (44)

 

=  =  =3D   0,0000 + 1,0600=  + (-0,1127)  =                      (45)

 

 

 

 

 

 

 

 

 

 

 

 <= /p>

Tabla = 8

Estimaciones de los parámetros y pruebas de diagnóstico para Brasil

 

Banco Bradesco

EGARCH (1,1)

Banco Bradesco

 TGARCH (1,1)

Metalúrgica

Gerdau  =

EGARCH (1,1)

Metalúrgica

Gerdau

TGARCH (1,1)

Atacadao

 

EGARCH (1,1)

Atacadao

 

TGARCH (1,1)

c

-0,0099

-0,0080

-0,0016

-0,0020

-0,0084

-0,0091

 

[0,0001]

[0,0019]

[0,7083]

[0,6887]

[0,0042]

[0,0000]

Var (-1)

-0,0101

0,0047

-0,1534

-0,0079

-0,0601

-0,0616

 

[0,9269]

[0,9646]

[0,1571]

[0,9267]

[0,5624]

[0,4259]

φ

-0,6369

0,0000

-0,3701

0,0002

-1,2289

0,0001

 

[0,0429]

[0,0513]

[0,0000]

[0,0008]

[0,1379]

[0,016]

η

0,1737

-0,0789

-0,2211

-0,2946

0,3685

-0,1443

 

[0,2383]

[0,4709]

[-0,0006]

0,0003

[0,1876]

[0,038]

λ

-0,3936

0,6422

-0,3152

0,4302

-0,4391

1,1299

 

[0,0025]

[0,0483]

[0,0000]

[0,0001]

[0,0046]

[0,0166]

θ

0,9202

0,7856

0,9101

0,9587

0,8662

0,6570

 

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

[0,0000]

0,0001

-0,0038

0,0163

-0,0020

0,0063

0,0009

Akaike

-3,8646

-3,8333

-3,0912

-3,1226

-4,2569

-4,2873

Schwarz

-3,7023

-3,6710

-2,9289

-2,9602

-4,0946

-4,1250

Hannan-Quinn

-3,7991

-3,7678

-3,0257

-3,0570

-4,1913

-4,2217

Notas.

 

Para el = caso de Brasil, las estimaciones del parámetro (λ) fueron significativas e indican que la pandemia afectó a la variabilidad de los precios de las acci= ones Banco Bradesco, Metalúrgica Gerdau y Atacadao. = La estimación de la varianza condicional frente al shock de la pandemia y en condiciones normales queda construida como:

 

=  = =3D   0,0000 + 0,7856=  + ( -0,0789 + 0,6422)  =                (46)

=  Banco Bradesco =3D   0,0000 + 0,7856=  + ( -0,0789)  =                                     (47)=

 

=  =  =3D   0,0002 + 0,9587=  + (-0,2946 + 0,4302)  =            (48)

=  Metalúrgica Gerdau =3D   0,0001 + 0,9587=  + (-0,2946)  =                                (49)

 

=  =  =3D   0,0001 + 0,6570=  + ( -0,1443 + 1,1299)  =                      =    (50)

 

=  Atacadao =3D   0,0000 + 0,6570=  + ( -0,1443)  =                                         <= span class=3DGramE>   (51)=

 

 

Los resultados presentados cuantifican la transmisión de volatilidad= de la pandemia COVID-19 a las acciones de empresas de Sudamérica, México y Est= ados Unidos.

 

 

 

 

 

Figura 2

Evolución de precios de las acciones de Sudamérica, México y Estados Unidos

Notas. La figura muestra la evolución de precios entre el 5 de enero hasta el 20 de mayo= de 2020. El estudio considera el análisis= de las acciones con mayor presencia bursátil de cada sector de los países que = se mencionan a continuación. Estados Unidos: JPMorgan Chase, Walmart, General Motors; Ecuador: Banco de Guayaquil, Corporación la Favorita, Natluk; Perú:  Banco BBVA, Empresa Siderúrgica del Perú, Unión Andina de Cementos; Colombia:  Bancolombia, Grupo Argos, Grupo Nutresa; Argentina:  Grupo Supervielle, Cablevisión, Aluar Aluminio; Chile: Banco Itaú, Falabella, Embotelladora Andina; México: Grupo Financiero Inbursa, Grupo Bimbo, América móvil; Brasil:  Banco Bradesco, Metalúrgica Gerdau, Atacadao. Fuente: obtenidas de las bols= as de valores de cada país. Elaboración propia.

 

 

 

 

 

 

 

 

 

 

 

 

 

Figura 3

 Rendimiento diario de las acciones de Sudamérica, México y Estados Unidos

Notas. La figura muestra el rendimiento diario de las acciones entre el 5 de e= nero hasta el 20 de mayo de 2020. El estudio considera el análisis de las accion= es con mayor presencia bursátil de cada sector de los países que se mencionan a continuación. Estados Unidos: JPMorgan Chase, Walmart, General Motors; Ecua= dor: Banco de Guayaquil, Corporación la Favorita, Natluk; Perú:  Banco BBVA, Empresa Siderúrg= ica del Perú, Unión Andina de Cementos; Colombia:  Bancolombia, Grupo Argos, Grupo Nutresa; Argentina:  Grupo Supervielle, Cablevisión, Aluar Aluminio; Chile: Banco Itaú, Falabella, Embotelladora Andina; México: Grupo Financiero Inbursa, Grupo Bimbo, América móvil; Brasil:  Banco Bradesco, Metalúrgica Gerdau, Atacadao. Fuente: obtenidas de las bolsas de valores = de cada país. Elaboración propia.

 

 

 

 

 

 

 

 

 

 

 

 

Figura 4<= /o:p>

 Distribución de frecuencia de las accion= es de Sudamérica, México y Estados Unidos

Notas.   acciones con mayor presencia bursátil de cada sector de los países q= ue se mencionan a continuación. Estados Unidos: JPMorgan Chase, Walmart, Gener= al Motors; Ecuador: Banco de Guayaquil, Corporación la Favorita, Natluk; Perú:  Banco BBVA, Empresa Siderúrgica del Perú, Unión Andina de Cementos; Colombia:  Bancolombia, Grupo Argos, Grupo Nutresa; Argentina:  Grupo Supervielle, Cablevisión, Aluar Aluminio; Chile: Banco Itaú, Falabella, Embotelladora Andina; México: Grupo Financiero Inbursa, Grupo Bimbo, América móvil; Brasil:  Banco Bradesco, Metalúrgica Gerdau, Atacadao. Fuente: obtenidas de las bols= as de valores de cada país. Elaboración propia.

 

Tabla 9

Pruebas de raíz unitaria

 

Método

Estadístico

Prob**

Secciones cruzadas

Obs

Ho: proceso raíz unitario conjunto.

 

 

 

 

Levin, Lin & Chu t*

-52.4277

0,0000

24

2149

Ho: proceso raíz unitario individual

 

 

 

 

Im, Pesaran y Shin W-stat

-48.3182

0,0000

24

2149

ADF – Fisher Chi cuadrado <= /p>

677.184

0,0000

24

2149

PP – Fisher Chi cuadrado

671.660

0,0000

24

2154

Notas. **Las probabilidades para las pruebas de Fisher se calculan utilizando= una distribución asintótica de chi cuadrado. Todas las demás pruebas asumen normalidad asintótica. Se observa que el estadístico de prueba de Dickey–Fuller Aumentado en valor absoluto es 677.184,= el estadístico PP con valor absoluto es 671.660 y el estadístico Pesaran y Shin con valor absoluto es de 48,3182, el p-value es menor al 5% de nivel de significancia lo cual conduce a rechazar la hipótesis nula, esto indica que las series utilizadas= son estacionarias.

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