Pronóstico de la tasa de cambio en Colombia (TRM) utilizando los modelos Arima-Garch durante el periodo 1992-2025
| dc.creator | Jiménez Méndez, Edgar Ricardo | |
| dc.creator | Aguilera Peña, Nicolas | |
| dc.creator | Cortés Villafradez, Raúl Alberto | |
| dc.date.accessioned | 2026-01-21T13:37:11Z | |
| dc.date.created | 2025-05-02 | |
| dc.description.abstract | Los pronósticos de variables económicas como la tasa de cambio o inflación son un instrumento esencial para las autoridades económicas, ya que basados en las expectativas de los analistas del mercado se toman decisiones que tienen efecto sobre la sociedad. Esta investigación presenta como objetivo determinar un modelo econométrico que sea útil para pronosticar la tasa de cambio del peso colombiano frente al dólar estadounidense USD/COP (TRM). Por esta razón se realizó un estudio cuantitativo mediante la aplicación de los principios ARIMA-GARCH usando como serie de tiempo datos diarios de la Tasa Representativa del Mercado (TRM) entre 1992 y 2022. Los resultados sugieren que el modelo calculado logró filtrar adecuadamente la información contenida en los rezagos mediante la prueba del Q-Stat sin problemas de autocorrelación simples al 5% de significancia estadística, lo que permitió establecer que los parámetros estimados para pronosticar la TRM son significativos. Por lo anterior, se concluye que el modelo tiene buena capacidad predictiva para el peso colombiano frente al dólar estadounidense en el corto plazo | |
| dc.description.abstractenglish | The economic figures forecast as exchange rate or the consumer price index are an essential tool for economic authorities, based on economic research analysts' expectations make decisions with effect in all society. This research has as objective to determinate a useful econometric model to forecast the Colombian exchange rate USD/COP (TRM). For this reason, it developed quantitative research applying ARIMA-GARCH models using time series data for TRM between 1992 and 2022. The results suggest that the model presented in this work achieves adequately filtered the information contained in lags using Q-Stat test, without simple análisis de los datos utilizados. Luego, se especifica el procedimiento de estimación del modelo y finalmente, se presentan los resultados y conclusiones. | |
| dc.format.extent | 23 páginas | |
| dc.format.mimetype | text/html | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12010/38830 | |
| dc.language.iso | es | |
| dc.relation.references | Arce, P., Antognini, J., Poller, W. K., & Salinas, L. (2019). Modelo basado en cointegración rápido y adaptativo para pronosticar series temporales financieras de alta frecuencia. Computación Económica, 54, 99–112. https://doi.org/10.1007/s10614-017-9691-7 | |
| dc.relation.references | Ayala, R. F., & Bucio, C. (2020). Modelo ARIMA aplicado al tipo de cambio peso-dólar en el periodo 2016-2017 mediante ventanas temporales deslizantes. Revista mexicana de economía y finanzas, 15(3), 331–354. https://doi.org/10.21919/remef.v15i3.466 | |
| dc.relation.references | Baghestani, H. (1992). On the formation of expected inflation under various conditions?: Some survey evidence. The Journal of Business, 65(2), 281–293. https://www.jstor.org/ stable/2353166 | |
| dc.relation.references | Box, G. E. P., & Jenkins, G. M. (1989). Time series analysis: Forecasting and control (1970). https://onlinelibrary.wiley.com/doi/10.1111/jtsa.12194?utm_source=researchgate | |
| dc.relation.references | Cao, X., & Zhao, Z. (2022). Research on stock index forecasting based on ARIMA-GARCH and SVM mixed model. Highlights in Science, Engineering and Technology, 1, 40–46. https://doi.org/10.54097/hset.v4i.843 | |
| dc.relation.references | Clauson, A. L. (1997). Forecasting retail food prices under current conditions. American Journal of Agricultural Economics, 79(5), 1669–1672. https://doi.org/10.2307/1244400 | |
| dc.relation.references | Clavijo, S. (2001). El régimen de flotación cambiaria en Colombia. (Vol. 7, Issue 2). https:// www.banrep.gov.co/es/el-regimen-flotacion-cambiaria-colombia | |
| dc.relation.references | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. ISBN 9780805802832. | |
| dc.relation.references | Dagum, E. B., & Morry, M. (1984). Basic issues on the seasonal adjustment of the canadian consumer price index. Journal of Business & Economic Statistics, 2(3), 250–259. https:// doi.org/10.1080/07350015.1984.10509392 | |
| dc.relation.references | Dritsaki, C. (2018). The performance of hybrid ARIMA-GARCH modeling and forecasting oil price. International Journal of Energy Economics and Policy, 8(3), 14–21. https://www. econjournals.com/index.php/ijeep/article/view/6437 | |
| dc.relation.references | Dyhrberg, A. H. (2015). Bitcoin, gold and the dollar - a GARCH volatility analysis. University College Dublin. School of Economics. http://hdl.handle.net/10197/7168 | |
| dc.relation.references | Gao, J. (2021). Research on stock price forecast based on ARIMA-GARCH model. E3S Web of Conferences, 292, 02030. https://doi.org/10.1051/e3sconf/202129202030 | |
| dc.relation.references | Ghani, I. M., & Rahim, H. A. (2019). Modeling and forecasting of volatility using ARMAGARCH: Case study on Malaysia natural rubber prices. IOP Conference Series: Materials Science and Engineering, 548(1). https://doi.org/10.1088/1757-899X/548/1/012023 | |
| dc.relation.references | Granger, P. N., & Jenkins, C. W. (1974). Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society, 137(2), 131–165. http://www.jstor.com/stable/2344546 | |
| dc.relation.references | Guo, W., Liu, Q., Luo, Z., & Tse, Y. (2022). Forecasts for international financial series with VMD algorithms. Journal of Asian Economics, 82. https://doi.org/10.1016/j. asieco.2022.101458 | |
| dc.relation.references | Jain, R. K. (1989). The seasonal adjustment procedures for the consumer price indexes: Some empirical results. Journal of Business & Economic Statistics, 7(4), 461–469. https://doi. org/10.1080/07350015.1989.10509758 | |
| dc.relation.references | Kang, H. (1986). Univariate ARIMA forecasts of defined variables. Journal of Business & Economic Statistics, 4(1), 81–86. https://doi.org/10.2307/1391389 | |
| dc.relation.references | Kučera, J., Kalinová, E., & Divoká, L. (2022). Profitability of current investments in stock indexes. Entrepreneurship and Sustainability Issues, 10(1), 420–434. https://doi. org/10.9770/jesi.2022.10.1(23) | |
| dc.relation.references | Kumari, S., & Gupta, J. (2022). Forecasting SGD-INR exchange return: An application of autoregressive integrated moving average. ANUSANDHAN - NDIM's Journal of Business and Management Research, 4(1), 16–22. https://doi.org/10.56411/anusandhan.2022. v4i1.16-22 | |
| dc.relation.references | Lee, T., Wang, H., Xi, Z., & Zhang, R. (2023). Density forecast of financial returns using decomposition and maximum entropy. Diario de Métodos Econométricos, 12(1), 57–83. https://doi.org/10.1515/jem-2020-0014 | |
| dc.relation.references | Lewis-Beck, M., Bryman, A., & Futing Liao, T. (2012). Box-Jenkins modeling. The SAGE Encyclopedia of Social Science Research Methods, 1–2. https://doi.org/10.4135/97814129 50589.n80 | |
| dc.relation.references | Li, Z., Han, J., & Song, Y. (2020). On the forecasting of high-frequency financial time series based on ARIMA model improved by deep learning. Journal of Forecasting, 39(7), 1081–1097. https://doi.org/10.1002/for.2677 | |
| dc.relation.references | Madrid, C., Pellegrini, S., Ruiz, E., & Espasa, A. (2007). The relationship between ARIMAGARCH and unobserved component models with GARCH disturbances. (Working Paper). https://ideas.repec.org/p/cte/wsrepe/ws072706.html | |
| dc.relation.references | Najamudin, M., & Fátima, S. (2022). Hybrid BRNN-ARIMA model for financial time series forecasting. Sukkur IBA Journal of Computing and Mathematical Sciences, 6(1), 62–71. https://doi.org/10.30537/sjcms.v6i1.1027 | |
| dc.relation.references | Pahlavani, M., & Reza, R. (2015). The comparison among ARIMA and hybrid ARIMA-GARCH models in forecasting the exchange rate of Iran. International Journal of Business and Development Studies, 7(1), 31–50. https://ijbds.usb.ac.ir/article_2198.html | |
| dc.relation.references | Peng, Z., Khan, F. U., Khan, F., Shaikh, P. A., Yonghong, D., Ullah, I., & Ullah, F. (2020). An application of hybrid models for weekly stock market index prediction: Empirical evidence from SAARC countries. Complexity in Financial Markets. https://doi. org/10.1155/2021/5663302 | |
| dc.relation.references | Pinčák, R., & Bartoš, E. (2015). With string model to time series forecasting. Physica A: Statistical Mechanics and its Applications, 436, 135–146. https://doi.org/10.1016/j. physa.2015.05.013 | |
| dc.relation.references | Shen, W., Guo, X., Wu, C., & Wu, D. (2011). Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge- Based Systems, 24(3), 378–385. https://doi.org/10.1016/j.knosys.2010.11.001 | |
| dc.relation.references | Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2022). Profit prediction using ARIMA, SARIMA and LSTM models in time series forecasting: A comparison. IEEE Access, 10, 124715– 124727. https://doi.org/10.1109/ACCESS.2022.3224938 | |
| dc.relation.references | Staffini, A. (2022). Stock price forecasting by a deep convolutional generative adversarial network. Frontiers in Artificial Intelligence, 5, 1–16. https://doi.org/10.3389/frai.2022.837596 | |
| dc.relation.references | Thomakos, D. D., & Bhattacharya, P. S. (2005). Forecasting Inflation, Industrial Output and Exchange Rates: A Template Study for India. Indian Economic Review, 40(2), 145–165. http://www.jstor.org/stable/29793841 | |
| dc.relation.references | Torres, L. E. (2014). Contraste entre modelos de Redes Neuronales Artificiales, GLM y GARCH en el pronóstico y análisis del tipo de cambio mexicano: 2000-2014. [Tesis de maestría, Repositorio Institucional Universidad Autónoma del Estado de México]. http://hdl. handle.net/20.500.11799/67002 | |
| dc.relation.references | Urrutia, M., & Llano, J. (2011). La crisis internacional y cambiaria de fin de siglo en Colombia. Desarrollo y Sociedad, 67, 11–48. https://doi.org/10.13043/dys.67.1 | |
| dc.relation.references | Vizek, M., & Broz, T. (2009). Modeling inflation in Croatia. Emerging Markets Finance and Trade, 45(6), 87–98. https://doi.org/10.2753/REE1540-496X450606 | |
| dc.relation.references | Wang, F., Li, M., Mei, Y., & Li, W. (2020). Time series data mining: A case study with big data analytics approach. IEEE Access, 8, 1–7. https://doi.org/10.1109/ACCESS.2020.2966553 | |
| dc.relation.references | Wang, Y., & Guo, Y. (2020). Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost. China Communications, 17(3), 205–221. https://doi.org/10.23919/JCC.2020.03.017 | |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.local | Abierto (Texto Completo) | |
| dc.subject | ARIMA | |
| dc.subject | GARCH | |
| dc.subject | Dólar | |
| dc.subject | Pronóstico | |
| dc.subject | Econometría | |
| dc.subject | Macroeconomía | |
| dc.subject.keyword | Exchange rate forecasts | |
| dc.subject.keyword | Econometric models | |
| dc.subject.keyword | Time series analysis | |
| dc.subject.lemb | Tasa de cambio - Pronósticos | |
| dc.subject.lemb | Modelos econométricos | |
| dc.subject.lemb | Series de tiempo - Análisis | |
| dc.title | Pronóstico de la tasa de cambio en Colombia (TRM) utilizando los modelos Arima-Garch durante el periodo 1992-2025 | |
| dc.type.coar | http://purl.org/coar/resource_type/c_6501 |