Published
2012-01-01
PATRONES DEL IGBC Y VALOR EN RIESGO: EVALUACIÓN DEL DESEMPEÑO DE DIFERENTES METODOLOGÍAS PARA DATOS INTRA-DÍA
INTRADAY-PATTERNS IN THE COLOMBIAN EXCHANGE MARKET INDEX AND VaR: EVALUATION OF DIFFERENT APPROACHES
Keywords:
apalancamiento, estimación de riesgo, finanzas, GARCH, rendimientos financieros (es)Leverage, Finance, GARCH model, Risk estimation, Stock returns (en)
El documento evalúa el desempeño de 16 métodos paramétricos, uno no paramétrico y uno semiparamétrico, para estimar el VaR (Valor en Riesgo) de un portafolio conformado por el Índice General de la Bolsa de Valores de Colombia (IGBC). El ejercicio se realiza analizando dos muestras de datos intra-día con una periodicidad de 10 minutos para los períodos 2006-2007 y 2008-2009. Los modelos paramétricos evaluados consideran la presencia o no de patrones de comportamiento, tales como: el efecto “Leverage”, el efecto día de la semana, el efecto hora y el efecto día-hora. Nuestros resultados muestran que para la primera muestra el mejor modelo es un TGARCH(1,1) sin el efecto día de la semana ni la hora del día y bajo el supuesto de una distribución t. Para la segunda muestra, 2008-2009, el método que presenta el mejor comportamiento corresponde al modelo GARCH(1,1), que tiene en cuenta el efecto del día y la hora. Estos dos modelos presentan una correcta cobertura condicional y menor función de pérdida.
This paper evaluates the performance of 16 different parametric, nonparametric and one semi-parametric specifications to calculate the Value at Risk (VaR) for the Colombian Exchange Market Index (IGBC). Using high frequency data (10-minute returns), we model the variance of the returns using GARCH and TGARCH models, that take in account the leverage effect, the day-of-the-week effect, and the hour-of-the-day effect. We estimate those models under two assumptions regarding returns’ behavior: Normal distribution and t distribution. This exercise is performed using two different ten-minute intraday samples: 2006-2007 and 2008-2009. For the first sample, we found that the best model is a TGARCH(1,1) without day-of the week or hour-of-the-day effects. For the 2008-2009 sample, we found that the model with the correct conditional VaR coverage would be the GARCH(1,1) with the day-of-the-week effect, and the hour-of-the-day effect. Both methods perform better under the t distribution assumption.
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Copyright (c) 2012 Revista Colombiana de Estadística

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