Título:

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

2020

2020

Autor (es):

De la Torre-Torres, Oscar V.;
Galeana-Figueroa, Evaristo &
Álvarez-García, José

Revista:

Mathematics

Volumen:

8

​Número:

6

DOI/URL:

Páginas:

942-964

Palabras clave:

American stock markets; Latin; Markov; Markovian chain; Switching; Switching GARCH; active investment; active stock trading; algorithmic trading; computational finance

Resumen:

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.

©2020 por Sitio web del Dr. Oscar De la Torre Torres. Creada con Wix.com