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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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Empirical Modeling of Latin American Stock and Forex Markets Returns and Volatility using Markov-Switching GARCH Models(Pontificia Universidad Católica del Perú. Departamento de Economía, 2017-03)Using a sample of weekly frequency of the stock and Forex markets returns series, we estimate a set of Markov-Switching-Generalized Autoregressive Conditional Heterocedasticity (MS-GARCH) models to a set of Latin American countries (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. The estimates are compared with a standard GARCH, MS and other models. The results show that the volatility persistence is captured differently 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 and a lower likelihood with the other model than MS-GARCH model. There is different behavior of the coefficients and the variance according the two regimes (high and low volatility) by each model in the Latin American stock and Forex markets. There are common episodes related to global international crises and also domestic events producing the different behavior in the volatility of each time series. Usando una muestra de frecuencia semanal de las series de retornos de los mercados bursátiles y cambiarios, estimamos un conjunto de modelos de heterocedasticidad condicional autorregresiva generalizada Markov-Switching (MS-GARCH) para un conjunto de países Latinoamericanos (Brasil, Chile, Colombia, México y Perú) con un enfoque basado tanto en los algoritmos de maximización de expectativas de Monte Carlo (MCEM) como en los de máxima verosimilitud de Monte Carlo (MCML). Las estimaciones se comparan con un modelos estándares de tipo GARCH, MS y otros. Los resultados muestran que la persistencia de la volatilidad se captura de forma diferente en los modelos MS y MS-GARCH. Los parámetros estimados con un modelo GARCH estándar exacerban la volatilidad en casi el doble en comparación con el modelo MS-GARCH y una menor verosimilitud con el otro modelo comparado con el modelo MS-GARCH. Hay un comportamiento diferente de los coeficientes y la varianza según los dos regímenes (alta y baja volatilidad) por cada modelo en los mercados bursátiles y cambiarios de América Latina. Hay episodios comunes relacionados con las crisis internacionales globales y también con los acontecimientos internos que producen los diferentes comportamientos en la volatilidad de cada serie temporal.