Bootstrap prediction in univariate volatility models with leverage effect

Carlos Trucíos () and Luiz Hotta

Mathematics and Computers in Simulation (MATCOM), 2016, vol. 120, issue C, 91-103

Abstract: The EGARCH and GJR-GARCH models are widely used in modeling volatility when a leverage effect is present in the data. Traditional methods of constructing prediction intervals for time series normally assume that the model parameters are known, and the innovations are normally distributed. When these assumptions are not true, the prediction interval obtained usually has the wrong coverage. In this article, the Pascual, Romo and Ruiz (PRR) algorithm, developed to obtain prediction intervals for GARCH models, is adapted to obtain prediction intervals of returns and volatilities in EGARCH and GJR-GARCH models. These adjustments have the same advantage of the original PRR algorithm, which incorporates a component of uncertainty due to parameter estimation and does not require assumptions about the distribution of the innovations. The adaptations show good performance in Monte Carlo experiments. However, the performance, especially in volatility prediction, can be very poor in the presence of an additive outlier near the forecasting origin. The algorithms are applied to the daily returns series of the GBP/USD exchange rates.

Keywords: Interval prediction; Volatility interval prediction; Interval prediction and outlier; Interval prediction in EGARCH model; Interval prediction in GJR-GARCH model (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:120:y:2016:i:c:p:91-103

DOI: 10.1016/j.matcom.2015.07.001

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