Mostrar el registro sencillo del ítem

Volatility estimators based on high-frequency information from the share capitalization index (Colcap) in Colombia

Estimadores de volatilidad basados en información de alta frecuencia del índice de capitalización accionaria (Colcap) en Colombia;
Estimadores da volatilidade com base na informação de alta frequência na taxa de capitalização acionária (Colcap) na Colômbia

dc.contributor.authorGalarza Melo, Edison
dc.contributor.authorFajardo Hoyos, Claudia Liceth
dc.coverage.spatialLat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.date.accessioned2024-01-23T15:56:43Z
dc.date.available2024-01-23T15:56:43Z
dc.date.created2021-07-30
dc.identifier.issn0120-6346
dc.identifier.urihttp://hdl.handle.net/11407/8227
dc.descriptionThe purpose of the article was to determine the behavior of the volatility of the Capitalization Index of the Colombian Stock Exchange (Colcap), in the period from January 17th, 2008 to April 30th, 2020. For the development of this work, the study employed the autoregressive conditional heteroscedasticity models proposed by Engle (1982) and Bollersev (1986), and the Egarch extension proposed by Nelson (1991) due to its wide application in researches that seek to determine the underlying risks in financial time series. The results suggest that the use of the GARCH (1,1) and Egarch (1,1) specifications are the most efficient to capture sudden changes in the volatility of the returns of the index, which becomes more noticeable in periods with the presence of External shocks such as the 2008 financial crisis, the oil price war and more recently the COVID-19 pandemic, causing higher levels of risk and uncertainty for investors. The Egarch extension has a positive skew coefficient for the present study, which means that unexpected announcements do not generate drastic changes in the variance of the returns.eng
dc.descriptionEl propósito del artículo es determinar el comportamiento de la volatilidad del Índice de Capitalización de la Bolsa de Valores de Colombia (Colcap) en el periodo del 17 de enero de 2008 al 30 de abril de 2020. Se utilizaron los modelos autorregresivos de heteroscedasticidad condicional propuestos por Engle (1982), Bollersev (1986) y la extensión Egarch planteada por Nelson (1991), por su amplia aplicación en investigaciones que buscan determinar los riesgos subyacentes en series de tiempo financieras. Los resultados sugieren que el uso de las especificaciones Garch (1,1) y Egarch (1,1), son los más eficientes para capturar los cambios repentinos en la volatilidad de los retornos del índice, que se hace más notoria en periodos con presencia de choques externos, tales como la crisis financiera de 2008, la guerra de precios del petróleo y más recientemente por la pandemia de la COVID-19, ocasionando mayores niveles de riesgo e incertidumbre para los inversionistas. En esta investigación, la extensión Egarch tiene coeficiente de asimetría positivo, lo que significa que ante anuncios inesperados no generarán cambios drásticos en la varianza de los retornos.spa
dc.descriptionO propósito do artigo é determinar o comportamento da volatilidade do índice de Capitalização da Bolsa de Valores da Colômbia (Colcap) no período de 17 de janeiro a 30 de abril de 2020. Foram usados os modelos autorregresivos de heteroscedasticidad condicional propostos por Engle (1982), Bollersev (1986) e a extensão Egarch apresentada por Nelson (1991), pela sua ampla aplicação em pesquisas que buscam determinar os riscos subjacentes em séries de tempo financeiras. Os resultados indicam que o uso das especificações Garch (1,1) y Egarch (1,1), são os mais eficientes para captar as alterações repentinas na volatilidade dos retornos do índice, que é mais notório nos períodos com presença de choques externos, tais como a crise financeira de 2008, a guerra dos preços do petróleo e mais recentemente a pandemia da Covid-19, ocasionando altos níveis de ricos e incertezas para os investidores. Nesta investigação, a extensão Egarch tem coeficiente de assimetria positivo, o que significa que ante anúncios inesperados não gerarão mudanças drásticas na variância dos retornos.por
dc.formatPDF
dc.format.extentp. 143-166
dc.format.mediumElectrónico
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad de Medellín
dc.relation.ispartofseriesSemestre Económico; Vol. 24 No. 56 (2021)
dc.relation.haspartSemestre Económico; Vol. 24 Núm. 56 enero-junio 2021
dc.relation.urihttps://revistas.udem.edu.co/index.php/economico/article/view/3720
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0*
dc.sourceSemestre Económico; Vol. 24 No. 56 (2021): (enero-junio); 143-166
dc.subjectVolatilityeng
dc.subjectGarcheng
dc.subjectEgarcheng
dc.subjectStock indiceseng
dc.subjectFinancial marketseng
dc.subjectVolatilidadspa
dc.subjectGarchspa
dc.subjectEgarchspa
dc.subjectÍndices bursátilesspa
dc.subjectMercados financierosspa
dc.subjectVolatilidadepor
dc.subjectGarchpor
dc.subjectEgarchpor
dc.subjectÍndices bolsistaspor
dc.subjectMercados financeirospor
dc.titleVolatility estimators based on high-frequency information from the share capitalization index (Colcap) in Colombiaeng
dc.titleEstimadores de volatilidad basados en información de alta frecuencia del índice de capitalización accionaria (Colcap) en Colombiaspa
dc.titleEstimadores da volatilidade com base na informação de alta frequência na taxa de capitalização acionária (Colcap) na Colômbiapor
dc.typearticle
dc.identifier.doihttps://doi.org/10.22395/seec.v24n56a6
dc.relation.citationvolume24
dc.relation.citationissue56
dc.relation.citationstartpage143
dc.relation.citationendpage166
dc.audienceComunidad Universidad de Medellín
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativas
dc.publisher.placeMedellín
dc.relation.referencesAcevedo, N. M., Jiménez, L. M. y Castaño, N. E. (2017). Relación de causalidad de variables macroeconómicas locales y globales sobre el índice Colcap. Espacios, 38(21), 38. http://www.revistaespacios.com/a17v38n21/a17v38n21p38.pdf
dc.relation.referencesAgudelo, D. y Gutierrez, A. (2011). Anuncios macroeconnmicos y mercados accionarios: El caso latinoamericano. Revista Latinoamericana de Administración, 48, 46-60. https://doi.org/10.2139/ssrn.2407178
dc.relation.referencesAhmad, N., Raheem Ahmed, R., Vveinhardt, J. y Streimikiene, D. (2016). Empirical Analysis of Stock Returns and Volatility: Evidence from Asian Stock Markets. Technological and Economic Development of Economy, 22(6), 808-829. https://doi.org/10.3846/20294913.2016.1213204
dc.relation.referencesAmin, A., Colman, A. y Grunske, L. (2012). An Approach to Forecasting QoS Attributes of Web Services Based on Arima and Garch Models. 2012 IEEE 19th International Conference on Web Services, 74-81. https://doi.org/10.1109/ICWS.2012.37/
dc.relation.referencesArbeláez, D. (2016). Efectos estacionales en los mercados de capitales de la Alianza del Pacífico. Estudios Gerenciales, 32(141), 358-368. https://doi.org/10.1016/j.estger.2016.10.002
dc.relation.referencesArboleda, S. S. (2017). Puede explicarse la estructura de dependencia del ´Indice General de la Bolsa de Valores de Colombia y Colcap por medio de modelos Switching de Markov con heterocedasticidad condicional? [tesis de maestría, Universidad Nacional de Colombia]. Repositorio Institucional UN. https://repositorio.unal.edu.co/handle/unal/59948
dc.relation.referencesBollsersev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
dc.relation.referencesBolsa de Valores de Colombia. (2018). Metodología para el cálculo del índice colcap. https://www.bvc.com.co/pps/tibco/portalbvc/Home/Mercados/descripciongeneral/indicesbursatiles?com.tibco.ps.pagesvc.action=updateRenderState&rp.currentDocumentID=5057504f_154e26bf23c_7ee30a0a600b&rp.revisionNumber=1&rp.attachmentPropertyName=Attachment&co
dc.relation.referencesBrugger, S. y Ortiz, E. (2012). Mercados accionarios y su relación con la economía real en América Latina. Revista Problemas del Desarrollo, 43(168), 63-93. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0301-70362012000100004
dc.relation.referencesChou, R. Y. (1988). Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch. Journal of Applied Econometrics, 3(4), 279-294. https://www.jstor.org/stable/2096644
dc.relation.referencesDuran, R. V., Lorenzo, A. V. y Ruiz, A. P. (2013). Un modelo Garch con asimetria condicional autorregresiva para modelar series de tiempo: una aplicacion para los rendimientos del Indice de Precios y Cotizaciones de la BMV. Munich personal Archive, 24. https://mpra.ub.unimuenchen.de/46328/1/MPRA_paper_46328.pdf
dc.relation.referencesEngle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987. https://doi.org/10.2307/1912773
dc.relation.referencesEngle, R. F. y Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778. https://www.scirp.org/(S(351jmbntvnsjt1aadkposzje))/reference/referencespapers.aspx?referenceid=2096151
dc.relation.referencesFama, E. y Malkiel, B. (1970). Efficient Capital Markets: A Review of the Theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
dc.relation.referencesFernandez, H. C. (2010). Egarch: un modelo asimétrico para estimar la volatilidad de series financieras. Revista Ingenierías Universidad de Medellín, 9(16), 45-60. https://revistas.udem.edu.co/index.php/ingenierias/article/view/240
dc.relation.referencesFlannery, M. J. y Protopapadakis, A. A. (2002). Macroeconomic Factors Do Influence Aggregate Stock Returns. The review of financial studies, 15(3), 751-782. https://doi.org/10.1093/rfs/15.3.751
dc.relation.referencesGeweke, J. (1986) Modeling the persistence of conditional variances: a comment. Econometric Review 5, 57–61.
dc.relation.referencesGyamerah, S. A. (2019). Modelling the Volatility of Bitcoin Returns Using Garch Models Modelling the Volatility of Bitcoin Returns Using Garch Models. Quantitative Finance an economics, 3(4), 739-753. https://doi.org/10.3934/QFE.2019.4.739
dc.relation.referencesInvesting. (2020). Datos históricos Colcap. https://es.investing.com/indices/colcap-historical-data
dc.relation.referencesKalovwe, S. K., Mwaniki, J. I. y Simwa, R. O. (2021). On Stock Returns Volatility and Trading Volume of the Nairobi Securities Exchange Index. RMS: Research in Mathematics & Statistics, 8(1), 1889765. https://doi.org/10.1080/27658449.2021.1889765
dc.relation.referencesKristjanpoller, W. y Minutolo, M. C. (2018). A Hybrid Volatility Forecasting Framework Integrating Garch , Artificial Neural Network , Technical Analysis and Principal Components Analysis. Expert Systems With Applications, 109, 1-11. https://doi.org/10.1016/j.eswa.2018.05.011
dc.relation.referencesLama, A., Jha, G. K., Paul, R. K. y Gurung, B. (2015). Modelling and Forecasting of Price Volatility: An Application of GARCH and Egarch models. Agricultural Economics Research Review, 28(1), 73-82. https://doi.org/10.5958/0974-0279.2015.00005.1
dc.relation.referencesLaopodis, N. T. (2011). Equity Prices and Macroeconomic Fundamentals: International Evidence. Journal of International Financial Markets, Institutions & Money, 21(2), 247-276. https://doi.org/10.1016/j.intfin.2010.10.006
dc.relation.referencesLawrence, G. R., Jagnnathan, R. y Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
dc.relation.referencesLeón, S. J. C. y Trespalacios, A. C. (2015). Factores macroeconómicos que influyen en la volatilidad del indice Colcap. https://repository.eafit.edu.co/bitstream/handle/10784/7749/SusanJuliette_LeonCristancho_2015.pdf?sequence=2&isAllowed=y
dc.relation.referencesLin, Z. (2018). Modelling and Forecasting the Stock Market Volatility of SSE Composite Index using Garch Models. Future Generation Computer Systems, 79, 960-972. https://doi.org/10.1016/jfuture.2017.08.033
dc.relation.referencesLiu, M., Lee, C. C. y Choo, W. C. (2021). The Role of High-Frequency Data in Volatility Forecasting: Evidence from the China Stock Market. Applied Economics, 53(22), 2500-2526. https://doi.org/10.1080/00036846.2020.1862747
dc.relation.referencesNelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260
dc.relation.referencesNiomy, T. y Nathaniel, S. P. (2019). Modeling Rates of Inflation in Nigeria: an Application of ARMA, Arima and Garch models. Munich Personal RePEc Archive, 1-29. https://mpra.ub.uni-muenchen.de/91351/
dc.relation.referencesPantula, S.G. (1986). Modeling the Persistence of Conditional Variances: a Comment. Econometric Review, 5, 71–74.
dc.relation.referencesQuesada, M. (2011). Análisis de series. Modelos heteroscedásticos [tesis de maestría, Universidad de Granada]. https://masteres.ugr.es/moea/pages/tfm1011/analisisdeseriesmodelosheterocedasticos/!
dc.relation.referencesRangel, J. G. (2011). Macroeconomic News , Announcements , and Stock Market Jump Intensity Dynamics. Journal of Banking & Finance, 35(5), 1263-1276. https://doi.org/10.1016/j.jbankfin.2010.10.009
dc.relation.referencesRuffo, A. y Costa, J. M. (2019). Volatildiad e inestabildiad financiera en los mercados de capitales latinoamericanos. Una ilustracion del efecto de contagio durante la crisis de hipotecas sub-prime. Revista de investigación en modelos financieros, 1, 1-22. http://157.92.136.232/index.php/RIMF/article/view/1546/2178
dc.relation.referencesSarwar, G. y Khan, W. (2016). The Effect of US Stock Market Uncertainty on Emerging Market Returns. Emerging Markets Finance and Trade, 53(7), 1-16. https://doi.org/10.1080/154049 6X.2016.1180592
dc.relation.referencesSu, C. (2010). Application of EGARCH Model to Estimate Financial Volatility of Daily Returns: The empirical case of China. University of Gutemberg. https://core.ac.uk/download/pdf/16327036.pdf
dc.relation.referencesSu, F. y Wang, L. (2020). Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets. Emerging Markets Finance and Trade, 56(14), 3252-3269. https://doi.org/10.1080/1540496X.2019.1574566
dc.relation.referencesZhang, D., Hu, M. y Ji, Q. (2020). Financial Markets Under the Global Pandemic of COVID-19. Finance Research Letters, 36. https://doi.org/10.1016/j.frl.2020.101528
dc.relation.referencesZhang, J., Lai, Y. y Lin, J. (2016). The day-of-the-Week effects of stock markets in different countries. Finance Research Letters, 20, 1-16. https://doi.org/10.1016/j.frl.2016.09.006
dc.rights.creativecommonsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.identifier.eissn2248-4345
dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículo científico
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Ficheros en el ítem

Thumbnail
Nombre:
Semestre_Economico_455.pdf
Tamaño:
499.6Kb
Formato:
PDF
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International