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The US Dollar and the Colombian Peso currency exchange rate (Tasa Representativa del Mercado, TRM) is a financial series characterized by periods of high volatility In this paper it was applied three classes of time series: the ARMA, ARMA-GARCH and Markov Switching (MS) models to represent the logreturns of the TRM between 2013-01-03 and 2020-07-02 The best models among several fitted models for each class were determined based on the Akaike and the Bayesian information criteria (AIC and BIC, respectively) and one-step forecasts in the period 2020-07-03 to 2020-07-31 The ARMA-GARCH model allowed a more precise description of the conditional variance than the ARMA model Furthermore, the MS model defined 3 regimes each one with its own AR process The regime with highest variability showed sporadic occurrences, and can be associated to three important events at global scale: the oil crisis in 2014, the US-China trade war in 2018, and the COVID-19 pandemics in march 2020 The results demonstrate the robustness of the models for forecasting in one-step or longer time windows © 2020 Copyright for this paper by its authors
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ICAI_Workshops,_ICAIW_2020_-_1st_International_Workshop_on_Applied_Artificial_Intelligence,_WAAI_2020|2nd_International_Workshop_on_Applied_Informatics_for_Economics,_Society_and_Development,_AIESD_2020|3rd_International_Workshop_on_Data_Engineering_and_Analytics,_WDEA_2020|3rd_International_Workshop_on_Smart_and_Sustainable_Cities,_WSSC_2020
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Time series models for the colombian TRM exchange rate
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