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