Stock market volatility forecasting is an important and interesting topic of research due to its impact on trading decisions. This behavior is particularly important in emerging economies in Latin America, and moreover, in the larger stock markets of this region (Brazil, Mexico, and Chile). The Latin American region is highly influenced by macroeconomic factors; therefore, it is relevant to discover ways in which the market index forecast accuracy can be improved. Thus, in this study, we present a novel methodology: first, we forecast the volatility of each market using different GARCH models. Then, we use Markov Switching to determine the states of external factors. Subsequently, these states are combined in a ANFIS model to determine individual impact on each index, and finally, we use an ANN algorithm to improve the forecast accuracy of the best GARCH model forecast with the combined effects of all the external factors. The results indicate that this methodology manages to improve prediction in terms of MAPE and RMSE, thus providing a more accurate volatility estimation. (C) 2018 Elsevier B.V. All rights reserved.