Modelación y co-movimientos de la tasa de cambio Colombiana, 2011-2017

Giuliana Maya Sierra; Nini Johana Marín Rodríguez

Modelación y co-movimientos de la tasa de cambio Colombiana, 2011-2017

Giuliana Maya Sierra, Nini Johana Marín Rodríguez

Grupo de Investigación en Ingeniería Financiera (GINIF)

Resultado de la investigación: Contribución a una revista › Artículo › revisión exhaustiva

Resumen

The exchange rate is influenced by multiple national and international macroeconomic factors, which generates high levels of uncertainty. The objective of this research is the construction of ARIMA-GARCH and ARIMAX-GARCH models as a tool for the forecast of the exchange rate in Colombia from the daily returns of the closing prices USD/COP and its analysis of dynamic correlation with some of the most explicative variables. The results suggest that the incorporation of significant exogenous variables within the ARIMAX-GARCH model with persistent correlation according to the DCC (Dinamic Conditional Correlation) model to the USD/COP pair generates out-of-sample forecasts with better performance than the ARIMA-GARCH univariate models.

Título traducido de la contribución	Modeling and comovements of the Colombian exchange rate, 2011-2017
Idioma original	Español
Páginas (desde-hasta)	301-341
Número de páginas	41
Publicación	Revista de Metodos Cuantitativos para la Economia y la Empresa
Volumen	28
Estado	Publicada - 1 ene 2019

Palabras clave

Correlation
Exchange rate
Forecast models
Macroeconomic fundamentals

Otros archivos y enlaces

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Citar esto

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AB - The exchange rate is influenced by multiple national and international macroeconomic factors, which generates high levels of uncertainty. The objective of this research is the construction of ARIMA-GARCH and ARIMAX-GARCH models as a tool for the forecast of the exchange rate in Colombia from the daily returns of the closing prices USD/COP and its analysis of dynamic correlation with some of the most explicative variables. The results suggest that the incorporation of significant exogenous variables within the ARIMAX-GARCH model with persistent correlation according to the DCC (Dinamic Conditional Correlation) model to the USD/COP pair generates out-of-sample forecasts with better performance than the ARIMA-GARCH univariate models.

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