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An Empirical Application of a Random Level Shifts Model with Time-Varying Probability and Mean Reversion to the Volatility of Latin-American Forex Markets Returns [Una aplicación empírica de un modelo de cambios de nivel aleatorios con probabilidades cambiantes y reversión a la media a la volatilidad de los retornos cambiarios en América Latina]

Listed author(s):
  • Gabriel Rodríguez

    ( Departamento de Economía de la PUCP del Perú)

  • José Carlos Gonzáles Tanaka

Following Xu and Perron (2014), this paper uses daily data for six Forex Latin American markets. Four models of the family of the Random Level Shift (RLS) model are estimated: a basic model where probabilities of level shift are driven by a Bernouilli variable but probability is constant; a model where varying probabilities are allowed and introduced via past extreme returns; a model with mean reversion mechanism; and a model incorporating these two features. Our results prove three striking features: first, the four RLS models fit well the data, with almost all the estimates highly significant; second, the long memory property disappears completely from the ACF, including the GARCH effects; and third, the forecasting performance is much better for the RLS models against an overall of four competitor models: GARCH, FIGARCH and two ARFIMA models. [Siguiendo el trabajo de Xu y Perron (2014), este documento utiliza datos diarios de volatilidades de retornos cambiarios para seis mercados de América Latina. Cuatro modelos del tipo Random Level Shifts (RLS) son estimados: un modelo básico donde las probabilidades de cambios de nivel son gobernadas por una variable del tipo Bernouilli pero dicha probabilidad es constante; un modelo donde las probabilidades son cambiantes en el tiempo y dependen de los retornos bursátiles extremos negativos del periodo anterior; un modelo con reversión a la media; y un modelo que incorpora los dos aspectos mencionados anteriormente. Los resultados sugieren tres importantes aspectos: el primero es que los cuatro modelos RLS ajustan bien los datos con prácticamente todos los estimados altamente significativos; segundo, la característica de larga memoria desaparece completamente de la ACF, incluyendo los efectos GARCH; y, tercero, la performance de los cuatro modelos en términos de predicción es buena contra diferentes modelos rivales como los modelos GARCH, FIGARCH, y dos modelos ARFIMA.]

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Paper provided by Departamento de Economía - Pontificia Universidad Católica del Perú in its series Documentos de Trabajo / Working Papers with number 2016-415.

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Length: 48 pages
Date of creation: 2016
Publication status: published
Handle: RePEc:pcp:pucwps:wp00415
Contact details of provider: Postal:
Av. Universitaria 1801, San Miguel, Lima, Perú

Phone: (511) 626-2000 ext. 4950, 4951
Fax: (511) 626-2874
Web page: http://departamento.pucp.edu.pe/economia/
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