In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

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In the present paper we test the benefits of using two-regime Markov-Switching (MS) models in the stock markets of the MSCI Andean index (Chile, Colombia and Perú). We tested this with either, constant, ARCH or GARCH variances and Gaussian or t-Student log-likelihood functions. By performing 996 weekly simulations from January 2000 to January 2019 with each MS model, we tested the next investment strategy for a U.S. dollar based investor: 1) to invest in the risk-free asset if the probability of being in the high-volatility regime at t+1 is higher than 50 % or 2) to do it in the stock market index otherwise. Our results suggest that the Gaussian MS-GARCH models are the most suitable to generate alpha in the Chilean stock market and the Gaussian MS-ARCH in the Colombian one. For the Peruvian case, we found that is preferable to perform passive investing instead of active trading.

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