Ítem
Acceso Abierto
Transformando datos en decisiones: volatilidad financiera y herramientas de análisis
| dc.creator | Dussán Téllez, Juliana | |
| dc.creator | Pérez González, Paula Valentina | |
| dc.date.accessioned | 2024-12-10T15:40:51Z | |
| dc.date.available | 2024-12-10T15:40:51Z | |
| dc.date.created | 2024 | |
| dc.date.issued | 2024-12-10 | |
| dc.description | En 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.extent | 62 pp | |
| dc.format.mimetype | application/pdf | |
| dc.format.tipo | Documento | spa |
| dc.identifier.doi | https://doi.org/10.48713/10336_44506 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/44506 | |
| dc.language.iso | spa | |
| dc.publisher | Universidad del Rosario | spa |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
| dc.rights.accesRights | info:eu-repo/semantics/openAccess | |
| dc.rights.acceso | Abierto (Texto Completo) | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.source.bibliographicCitation | Aizenman, 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.bibliographicCitation | Arouri, 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.bibliographicCitation | Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. | |
| dc.source.bibliographicCitation | Baum, 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.bibliographicCitation | Bauwens, L., & Laurent, S. (2005). A new class of GARCH models. Journal of Financial Econometrics, 3(2), 232-272. | |
| dc.source.bibliographicCitation | Bernanke, 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.bibliographicCitation | Bollerslev, 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.bibliographicCitation | Bollerslev, T. (2023). A condensed overview of GARCH models. Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbac015 | |
| dc.source.bibliographicCitation | Bordo, 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.bibliographicCitation | Bluecinante. (2024, Octubre, 24). ¿Cuál es la diferencia entre HOMOCEDÁSTICO y HETEROCEDÁSTICO?. YouTube. https://www.youtube.com/watch?v=KK8pSgDRsXk | |
| dc.source.bibliographicCitation | Canva. (n.d.). Canva [Plataforma en línea]. Canva. Recuperado el 27 de noviembre de 2024, de https://www.canva.com | |
| dc.source.bibliographicCitation | Cappiello, L., Engle, R. F., & Sheppard, K. (2003). Asymmetric dynamics in the correlations of global equity and bond returns. | |
| dc.source.bibliographicCitation | Chen, 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.bibliographicCitation | Choi, W. (2020). Climate policy uncertainty and the cost of capital. Journal of Environmental Economics and Management, 102, 102342. | |
| dc.source.bibliographicCitation | Clements, M. P., & Hendry, D. F. (1998). Forecasting Economic Time Series. Cambridge University Press. | |
| dc.source.bibliographicCitation | CRAN. (2023). The Comprehensive R Archive Network (CRAN). https://cran.r-project.org/ | |
| dc.source.bibliographicCitation | Dai, 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.bibliographicCitation | Engle, 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.bibliographicCitation | Engle, R. F. (2004). Risk and Volatility: Econometric Models and Financial Practice. The American Economic Review, 94(3), 405-420. | |
| dc.source.bibliographicCitation | Engle, R. F. (2009). GARCH Models with Time-Varying Volatility and Correlations. Journal of Business & Economic Statistics, 27(4), 505-521. | |
| dc.source.bibliographicCitation | Engle, 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.bibliographicCitation | Engle, 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.bibliographicCitation | Garrett, T. A., & Liu, X. (2023). Climate change policy uncertainty and the cost of capital. Review of Financial Studies, 36(1), 275-314. | |
| dc.source.bibliographicCitation | Ghirelli, 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.bibliographicCitation | Hong, 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.bibliographicCitation | Huang, 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.bibliographicCitation | Huang, 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.bibliographicCitation | Ihaka, R., & Gentleman, R. (1996). R: A Language for Data Analysis and Graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314. | |
| dc.source.bibliographicCitation | Ivorra Carlos. (s.f). Matemáticas II: Apuntes de teoría. Universidad de Valencia. Facultad de Economía. | |
| dc.source.bibliographicCitation | Koenker, R., & Machado, J. A. F. (1999). Goodness of fit and residual analysis for GARCH models. Journal of Time Series Analysis. | |
| dc.source.bibliographicCitation | F. Hernández. (n.d.). Residuals standardized QQ plot. Retrieved from https://fhernanb.github.io/libro_regresion/images/qq_residuales_estandarizados.png | |
| dc.source.bibliographicCitation | Liu, 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.bibliographicCitation | McAleer, M., & Yu, J. (2006). Estimation and inference for GARCH models: A review. Statistical Papers. | |
| dc.source.bibliographicCitation | Mokni, 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.bibliographicCitation | Perilla, 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.bibliographicCitation | PoSIt | the Open-Source Data Science Company. (2024, 4 septiembre). Posit. https://posit.co/ | |
| dc.source.bibliographicCitation | R Core Team. (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ | |
| dc.source.bibliographicCitation | Roldán, P. N. (2022, 24 noviembre). Modelo econométrico - Definición, qué es y concepto | Economipedia. https://economipedia.com/definiciones/modelo-econometrico.html | |
| dc.source.bibliographicCitation | Sadorsky, 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.bibliographicCitation | Tian, 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.bibliographicCitation | Venables, W. N., Smith, D. M., & the R Core Team. (2013). An Introduction to R. R Foundation for Statistical Computing. | |
| dc.source.bibliographicCitation | Wang, 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.instname | instname:Universidad del Rosario | spa |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | spa |
| dc.subject | Economía | |
| dc.subject | Medio ambiente | |
| dc.subject | Modelos de regresión | |
| dc.subject | Volatilidad | |
| dc.subject | Modelo Garch Midas | |
| dc.title | Transformando datos en decisiones: volatilidad financiera y herramientas de análisis | |
| dc.type | workingPaper | |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | |
| dc.type.spa | Documento de trabajo |
Archivos
Bloque original
1 - 1 de 1
Cargando...
- Nombre:
- Cartilla_transformando_Datos_en_Decisiones_Volatilidad_Financiera_y_Herramientas_de_Analisis.pdf
- Tamaño:
- 6.69 MB
- Formato:
- Adobe Portable Document Format
- Descripción:



