An empirical applicatin of a random level shift model with time-varying probability and mean reversion to the volatility of Latin-America forex market returns
Following Xu and Perron (2014), this paper uses daily data for six Forex Latin American markets (Argentina, Brazil, Chile, Colombia, Mexico and Peru). 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 Berno...
| Autor Principal: | Gonzáles Tanaka, José Carlos |
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| Formato: | Tesis de Licenciatura |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Acceso en línea: |
http://tesis.pucp.edu.pe/repositorio/handle/123456789/8482 |
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| Sumario: |
Following Xu and Perron (2014), this paper uses daily data for six Forex Latin American markets (Argentina, Brazil, Chile, Colombia, Mexico and Peru). 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 last two features. Our results prove three striking features: rst, the four RLS models t well the data, with almost all the estimates highly signi cant; second, the long memory property disappears completely from the ACF, including the GARCH e¤ects; 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. |
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