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Item type:Publication, An Application of a Short Memory Model With Random Level Shifts to the Volatility of Latin American Stock Market Returns(Pontificia Universidad Católica del Perú. Departamento de Economía, 2014)Empirical research indicates that the volatility of stock return time series have long memory. However, it has been demonstrated that short memory processes contaminated with random level shifts can often be confused as being long memory. Often this feature is referred to as spurious long memory. This paper represents an empirical study of the random level shift (RLS) model using the approach of Lu and Perron (2010) and Li and Perron (2013) for the volatility of daily stocks returns data for five Latin American countries. The RLS model consists of the sum of a short term memory component and a level shift component, where the level shift component is governed by a Bernoulli process with a shift probability α. The estimation results suggest that the level shifts in the volatility of daily stocks returns data are infrequent but once they are taken into account, the long memory characteristic and the GARCH effects disappear. An out-of-sample forecasting exercise is also provided. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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.