Empirical Modeling of Latin American Stock and Forex Markets Returns and Volatility using Markov-Switching GARCH Models

Using a sample of weekly frequency of the stock and Forex markets returns series, we estimate a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedasticity (MS-GARCH) models to a set of Latin American countries (Brazil, Chile, Colombia, Mexico and Peru) with an approach based on both the Monte Carlo Expectation-Maximization (MCEM) and Monte Carlo Maximum Likelihood (MCML) algorithms. The estimates are compared with a standard GARCH, MS and other models. The results show that the volatility persistence is captured differently in the MS and MS-GARCH models. The estimated parameters with a standard GARCH model exacerbates the volatility in almost double compared to MS-GARCH model and a lower likelihood with the other model than MS-GARCH model. There is different behavior of the coefficients and the variance according the two regimes (high and low volatility) by each model in the Latin American stock and Forex markets. There are common episodes related to global international crises and also domestic events producing the different behavior in the volatility of each time series.

Keywords

Latin-American Forex market, GARCH Models, MS-GARCH Models, Returns, Volatility

JEL Classification

C22, C52, C53