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Prof. Dr. Evaristo Galeana-Figueroa
Faculty of Accounting and Management, Saint Nicholas and Hidalgo Michoacán State University (UMSNH), Morelia 58030, Mexico

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Journal article
Published: 08 June 2020 in Mathematics
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In the present paper, we review the use of two-state, Generalized Auto Regressive Conditionally Heteroskedastic Markovian stochastic processes (MS-GARCH). These show the quantitative model of an active stock trading algorithm in the three main Latin-American stock markets (Brazil, Chile, and Mexico). By backtesting the performance of a U.S. dollar based investor, we found that the use of the Gaussian MS-GARCH leads, in the Brazilian market, to a better performance against a buy and hold strategy (BH). In addition, we found that the use of t-Student MS-ARCH models is preferable in the Chilean market. Lastly, in the Mexican case, we found that is better to use Gaussian time-fixed variance MS models. Their use leads to the best overall performance than the BH portfolio. Our results are of use for practitioners by the fact that MS-GARCH models could be part of quantitative and computer algorithms for active trading in these three stock markets.

ACS Style

Oscar V. De La Torre-Torres; Evaristo Galeana-Figueroa; José Álvarez-García. Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets. Mathematics 2020, 8, 942 .

AMA Style

Oscar V. De La Torre-Torres, Evaristo Galeana-Figueroa, José Álvarez-García. Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets. Mathematics. 2020; 8 (6):942.

Chicago/Turabian Style

Oscar V. De La Torre-Torres; Evaristo Galeana-Figueroa; José Álvarez-García. 2020. "Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets." Mathematics 8, no. 6: 942.