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    Modelamiento de la volatilidad de las bolsas de valores de América Latina: Probabilidades variables y reversión promedio en un modelo de cambios de nivel randomizado.
    (Pontificia Universidad Católica del Perú. Departamento de Economía, 2015)
    Following Xu and Perron (2014), we applied the extended RLS model to the daily stock market returns of Argentina, Brazil, Chile, Mexico and Peru. This model replaces the constant probability of level shifts for the entire sample with varying probabilities that record periods with extremely negative returns; and furthermore, it incorporates a mean reversion mechanism with which the magnitude and the sign of the level shift component will vary in accordance with past level shifts that deviate from the long-term mean. Therefore, four RLS models are estimated: the basic RLS, the RLS with varying probabilities, the RLS with mean reversion, and a combined RLS model with mean reversion and varying probabilities. The results show that the estimated parameters are highly signi cant, especially that of the mean reversion model. An analysis is also performed of ARFIMA and GARCH models in the presence of level shifts, which shows that once these shifts are taken into account in the modeling, the long memory characteristics and GARCH e¤ects disappear. Our forecasting analysis con firms that the RLS models are more accurate than other classic long-memory models.
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    Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models
    (Pontificia Universidad Católica del Perú. Departamento de Economía, 2020-02)
    Seven GARCH and stochastic volatility (SV) models are used to model and compare empirically the volatility of returns on four commodities: gold, copper, oil, and natural gas. The results show evidence of fat tails and random jumps created by supply/demand imbalances, international instability episodes, geopolitical tensions, and market speculation, among other factors. We also find evidence of a leverage effect in oil and copper, resulting from their dependence on world economic activity; and of an inverse leverage effect in gold and natural gas, consistent with the formerís role as safe asset and with uncertainty about the latterís future supply. Additionally, in most cases there is no evidence of an impact of volatility on the mean. Finally, we find that the best-performing return volatility models are GARCH-t for gold, SV-t for copper and oil, and SV with leverage effects (SV-L) for natural gas.