Empirical modelling of latin american stock markets returns and volatility using Markov - Switching garch models
Using a sample of weekly frequency of the stock markets returns series, we estimate a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedastic- ity (MS-GARCH) models to a set of Latin American countries (Argentina, Brazil, Chile, Colombia, Mexico and Peru) with an approach ba...
| Autor Principal: | Ataurima Arellano, Miguel |
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| Formato: | Tesis de Maestría |
| Idioma: | Inglés |
| Publicado: |
Pontificia Universidad Católica del Perú
2017
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| Materias: | |
| Acceso en línea: |
http://tesis.pucp.edu.pe/repositorio/handle/123456789/8096 |
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| Sumario: |
Using a sample of weekly frequency of the stock markets returns series, we estimate
a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedastic-
ity (MS-GARCH) models to a set of Latin American countries (Argentina, Brazil,
Chile, Colombia, Mexico and Peru) with an approach based on both the Monte Carlo
Expectation-Maximization (MCEM) and Monte Carlo Maximum Likelihood (MCML)
algorithms suggested by Augustyniak (2014). The estimates are compared with a stan-
dard GARCH, MS and other models. The results show that the volatility persistence
is captured di¤erently in the MS and MS-GARCH models. The estimated parameters
with a standard GARCH model exacerbates the volatility in almost double compared
to MS-GARCH model. There is di¤erent behavior of the coe¢ cients and the variance
according the two regimes (high and low volatility) by each model in the Latin Amer-
ican stock markets. There are common episodes related to global international crises
and also domestic events producing the di¤erent behavior in the volatility of each time
series. |
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