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

Author

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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)

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.

Suggested Citation

  • 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, MDPI, vol. 8(6), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:942-:d:368746
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