Markov-Switching Stochastic Processes in an Active Trading Algorithm in the Main Latin-American Stock Markets

Oscar V. De la Torre-Torres, Evaristo Galeana-Figueroa and José Álvarez-García
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Oscar V. De la Torre-Torres: Faculty of Accounting and Management, Saint Nicholas and Hidalgo Michoacán State University (UMSNH), 58030 Morelia, Mexico
Evaristo Galeana-Figueroa: Faculty of Accounting and Management, Saint Nicholas and Hidalgo Michoacán State University (UMSNH), 58030 Morelia, Mexico
José Álvarez-García: Financial Economy and Accounting Department, Faculty of Business, Finance and Tourism, University of Extremadura, 10071 Cáceres, Spain

Mathematics, 2020, vol. 8, issue 6, 1-23

Abstract: 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.

Keywords: Markov-Switching; Markov-Switching GARCH; Markovian chain; algorithmic trading; active stock trading; active investment; Latin-American stock markets; computational finance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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