Using a sample of weekly frequency of the stock markets returns series, we estimate a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedastic- ity (MS-GARCH) models to a set of Latin American countries (Argentina, 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 suggested by Augustyniak (2014). The estimates are compared with a stan- dard GARCH, MS and other models. The results show that the volatility persistence is captured di¤erently 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. There is di¤erent behavior of the coe¢ cients and the variance according the two regimes (high and low volatility) by each model in the Latin Amer- ican ...

Using a sample of weekly frequency of the stock markets returns series, we estimate a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedastic- ity (MS-GARCH) models to a set of Latin American countries (Argentina, 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 suggested by Augustyniak (2014). The estimates are compared with a stan- dard GARCH, MS and other models. The results show that the volatility persistence is captured di¤erently 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. There is di¤erent behavior of the coe¢ cients and the variance according the two regimes (high and low volatility) by each model in the Latin Amer- ican ...

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 mod...

The study of the yield curve has been a topic that interested economists for a long time since the term structure of interest rates is an important transmission channel of monetary policy to inflation and real activity. In this paper, following Ang and Piazzesi (2003), we study the relevance of macroeconomic factors on Peruvian sovereign yield curve through an Affine Term Structure model for the period from November 2005 to December 2015. We estimate a Gaussian model to understand the joint dynamics of macro variables ―inflation and real activity factors― and Peruvian bond yields in a multifactor model of the term structure. Risk premium are modeled as time varying and depend on both observable and unobservable factors. A Vector Autoregressive (VAR) model is estimated considering no-arbitrage assumptions, which let us to derive Impulse Response Functions and Variance Decompositions. ...