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Transformando datos en decisiones: volatilidad financiera y herramientas de análisis

dc.creatorDussán Téllez, Juliana
dc.creatorPérez González, Paula Valentina
dc.date.accessioned2024-12-10T15:40:51Z
dc.date.available2024-12-10T15:40:51Z
dc.date.created2024
dc.date.issued2024-12-10
dc.descriptionEn el presente documento se expondrá la volatilidad como medio de análisis de la variabilidad de los precios de los activos financieros, la cual desempeña un papel fundamental en los mercados financieros ya que influye directamente en la toma de decisiones de los inversionistas, de los entes reguladores que se encargan de la mitigación de riesgos ambientales, económicos, y la valoración de activos. La modelización de esta variable es esencial para su predicción, debido a que permite comprender los posibles escenarios a futuro y así crear estrategias y tácticas con anticipación que disminuyan los riesgos del mercado.
dc.format.extent62 pp
dc.format.mimetypeapplication/pdf
dc.format.tipoDocumentospa
dc.identifier.doihttps://doi.org/10.48713/10336_44506
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/44506
dc.language.isospa
dc.publisherUniversidad del Rosariospa
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.source.bibliographicCitationAizenman, J., & Jinjarak, Y. (2019). Policy uncertainty and the international transmission of economic shocks. International Review of Economics & Finance. https://doi.org/10.1016/j.iref.2018.10.013
dc.source.bibliographicCitationArouri, M. H., Ben Youssef, A., & Jawadi, F. (2010). Does climate change policy uncertainty lead to financial market instability? Economics Letters, 109(2), 120-123.
dc.source.bibliographicCitationBaker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
dc.source.bibliographicCitationBaum, C. F., Caglayan, M., & Ozturk, H. (2010). A GARCH-MIDAS model of inflation uncertainty and economic growth in the UK. Journal of Business & Economic Statistics, 28(2), 277-290. https://doi.org/10.1198/jbes.2009.06039
dc.source.bibliographicCitationBauwens, L., & Laurent, S. (2005). A new class of GARCH models. Journal of Financial Econometrics, 3(2), 232-272.
dc.source.bibliographicCitationBernanke, B. S. (1983). Oil shocks and macroeconomic performance. The Review of Economic Studies, 50(2), 291-308. https://doi.org/10.2307/2297431
dc.source.bibliographicCitationBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
dc.source.bibliographicCitationBollerslev, T. (2023). A condensed overview of GARCH models. Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbac015
dc.source.bibliographicCitationBordo, M. D., Erceg, C. J., & Lindner, A. (2022). The role of policy uncertainty in the macroeconomic transmission of financial shocks. Journal of Monetary Economics. https://doi.org/10.1016/j.jmoneco.2022.01.004
dc.source.bibliographicCitationBluecinante. (2024, Octubre, 24). ¿Cuál es la diferencia entre HOMOCEDÁSTICO y HETEROCEDÁSTICO?. YouTube. https://www.youtube.com/watch?v=KK8pSgDRsXk
dc.source.bibliographicCitationCanva. (n.d.). Canva [Plataforma en línea]. Canva. Recuperado el 27 de noviembre de 2024, de https://www.canva.com
dc.source.bibliographicCitationCappiello, L., Engle, R. F., & Sheppard, K. (2003). Asymmetric dynamics in the correlations of global equity and bond returns.
dc.source.bibliographicCitationChen, Z., Zhang, L., & Weng, C. (2023). Does climate policy uncertainty affect Chinese stock market volatility? International Review of Economics & Finance, 84, 369-381.
dc.source.bibliographicCitationChoi, W. (2020). Climate policy uncertainty and the cost of capital. Journal of Environmental Economics and Management, 102, 102342.
dc.source.bibliographicCitationClements, M. P., & Hendry, D. F. (1998). Forecasting Economic Time Series. Cambridge University Press.
dc.source.bibliographicCitationCRAN. (2023). The Comprehensive R Archive Network (CRAN). https://cran.r-project.org/
dc.source.bibliographicCitationDai, Z., & Zhang, X. (2023). Climate policy uncertainty and risks taken by the bank: Evidence from China. International Review Of Financial Analysis (Online)/International Review Of Financial Analysis, 87, 102579. https://doi.org/10.1016/j.irfa.2023.102579
dc.source.bibliographicCitationEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773
dc.source.bibliographicCitationEngle, R. F. (2004). Risk and Volatility: Econometric Models and Financial Practice. The American Economic Review, 94(3), 405-420.
dc.source.bibliographicCitationEngle, R. F. (2009). GARCH Models with Time-Varying Volatility and Correlations. Journal of Business & Economic Statistics, 27(4), 505-521.
dc.source.bibliographicCitationEngle, R. F., & Kelly, B. (2013). Dynamic equicorrelation. Journal of Business & Economic Statistics, 31(2), 252-268. https://doi.org/10.1080/07350015.2012.743025
dc.source.bibliographicCitationEngle, R. F., & Lee, J. (1999). A long-term component of volatility. In R. Engle & H. White (Eds.), Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger (pp. 237-270). Oxford University Press.
dc.source.bibliographicCitationGarrett, T. A., & Liu, X. (2023). Climate change policy uncertainty and the cost of capital. Review of Financial Studies, 36(1), 275-314.
dc.source.bibliographicCitationGhirelli, C., Ghirelli, M., & Trani, T. (2021). Economic policy uncertainty and financial market volatility: Evidence from the United States. Journal of Financial Stability. https://doi.org/10.1016/j.jfs.2021.100873
dc.source.bibliographicCitationHong, L., Miao, J., & Wu, T. (2023). GARCH-MIDAS model for emerging markets: A study on Brazil and South Africa. Emerging Markets Review, 45, 100-115. https://doi.org/10.1016/j.ememar.2023.100115
dc.source.bibliographicCitationHuang, H., Ali, S., & Solangi, Y. A. (2023). Analysis of the Impact of Economic Policy Uncertainty on Environmental Sustainability in Developed and Developing Economies. Sustainability, 15(7), 5860. https://doi.org/10.3390/su15075860
dc.source.bibliographicCitationHuang, H., Ali, S., & Solangi, Y. A. (2023). The impact of economic policy uncertainty on emerging market economies. Emerging Markets Review. https://doi.org/10.1016/j.ememar.2022.100908
dc.source.bibliographicCitationIhaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.
dc.source.bibliographicCitationIvorra Carlos. (s.f). Matemáticas II: Apuntes de teoría. Universidad de Valencia. Facultad de Economía.
dc.source.bibliographicCitationKoenker, R., & Machado, J. A. F. (1999). Goodness of fit and residual analysis for GARCH models. Journal of Time Series Analysis.
dc.source.bibliographicCitationF. Hernández. (n.d.). Residuals standardized QQ plot. Retrieved from https://fhernanb.github.io/libro_regresion/images/qq_residuales_estandarizados.png
dc.source.bibliographicCitationLiu, J., & Wang, H. (2022). Economic policy uncertainty and corporate finance: Evidence from emerging markets. Journal of Corporate Finance. https://doi.org/10.1016/j.jcorfin.2021.102157
dc.source.bibliographicCitationMcAleer, M., & Yu, J. (2006). Estimation and inference for GARCH models: A review. Statistical Papers.
dc.source.bibliographicCitationMokni, K., Hedhili Zaier, L., Youssef, M., & Ben Jabeur, S. (2024). Quantile connectedness between the climate policy and economic uncertainty: Evidence from the G7 countries. Journal of Environmental Management, 351, 119826. https://doi.org/10.1016/j.jenvman.2023.119826
dc.source.bibliographicCitationPerilla, M. (2023). The impact of financial volatility on environmental investment. Journal of Environmental Economics and Management, 106, 102-117. https://doi.org/10.1016/j.jeem.2023.102117
dc.source.bibliographicCitationPoSIt | the Open-Source Data Science Company. (2024, 4 septiembre). Posit. https://posit.co/
dc.source.bibliographicCitationR Core Team. (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
dc.source.bibliographicCitationRoldán, P. N. (2022, 24 noviembre). Modelo econométrico - Definición, qué es y concepto | Economipedia. https://economipedia.com/definiciones/modelo-econometrico.html
dc.source.bibliographicCitationSadorsky, P. (2012). Modeling renewable energy company risk. Energy Policy, 40, 39-48. Salisu, A. A., Moshiri, S., & Zhuang, X. (2022). Volatility modeling in emerging markets: A comprehensive review. Journal of International Financial Markets, Institutions and Money, 79, 101548. https://doi.org/10.1016/j.intfin.2022.101548
dc.source.bibliographicCitationTian, L., Sun, Y., & Zhang, X. (2022). Climate policy uncertainty and the cost of capital: Evidence from China's carbon emissions trading scheme. Journal of Business Ethics, 1- 21.
dc.source.bibliographicCitationVenables, W. N., Smith, D. M., & the R Core Team. (2013). An Introduction to R. R Foundation for Statistical Computing.
dc.source.bibliographicCitationWang, H., & Li, J. (2023). Application of GARCH-MIDAS in commodity markets: Evidence from Mexico. Commodities and Financial Analysis, 33(1), 45-60. https://doi.org/10.1016/j.cfa.2023.100001
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subjectEconomía
dc.subjectMedio ambiente
dc.subjectModelos de regresión
dc.subjectVolatilidad
dc.subjectModelo Garch Midas
dc.titleTransformando datos en decisiones: volatilidad financiera y herramientas de análisis
dc.typeworkingPaper
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de trabajo
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