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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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Application of a Random Level Shifts Model to the Volatility of Peruvian Stock and Exchange Rates Returns(Pontificia Universidad Católica del Perú. Departamento de Economía, 2014)The literature has shown that the volatility of Stock and Forex rate market returns shows the characteristic of long memory. Another fact that is shown in the literature is that this feature may be spurious and volatility actually consists of a short memory process contaminated with random level shifts. In this paper, we follow the approach of Lu and Perron (2010) and Li and Perron (2013) estimating a model of random level shifts (RLS) to the logarithm of the absolute value of Stock and Forex returns. The model consists of the sum of a short term memory component and a component of level shifts. The second component is speci.ed as the cumulative sum of a process that is zero with probability 1- α and is a random variable with probability α. The results show that there are level shifts that are rare but once they are taken into account, the characteristic or property of long memory disappears. Also, the presence of GARCH e¤ects is eliminated when included or deducted level shifts. An exercise of out-of-sample forecasting shows that the RLS model has better performance than traditional models for modeling long memory such as the models ARFIMA (p,d,q). - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modelos univariados de hetero-esquedasticidad condicional autoregresiva Aplicación a los retornos del mercado de valores en el Perú.(Pontificia Universidad Católica del Perú. Departamento de Economía, 2015)An extensive family of univariate models of autoregressive conditional heteroskedasticity is applied to Peru’s daily stock market returns for the period January 3, 1992 to March 30, 2012 (5053 observations) with four different specifications related to the distribution of the disturbance term. This concerns capturing the asymmetries of the behavior of the volatility, as well as the presence of heavy tails in these time series. Using different statistical tests and different criteria, the results show the following: (i) the FIGARCH (1,1)-t is the best model among all symmetric models while the FIEGARCH (1,1)-Sk is selected from the class of asymmetrical models. Also, the model FIAPARCH (1,1)-t is selected from the class of asymmetric power models; (ii) the three models capture well the behavior of the conditional volatility; (iii) the model FIEGARCH (1,1)-Sk is the one with the best performance in terms of prediction; (iv) however, the empirical distribution of the standardized residuals shows that the behavior of the tails is not well captured by either model; (v) the three models suggest the presence of long memory with estimates of the fractional parameter close to the nonstationarity region.