Estimating and Forecasting GARCH Volatility in the Presence of Outiers

M. Angeles Carnero, Daniel Peña and Esther Ruiz ()

Working Papers. Serie AD from Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie)

Abstract: The main goal when fitting GARCH models to conditionally heteroscedastic time series is to estimate the underlying volatilities. It is well known that outliers affect the estimation of the GARCH parameters. However, little is known about their effects when estimating volatilities. In this paper, we show that when estimating the volatility by using Maximum Likelihood estimates of the parameters, the biases incurred can be very large even if estimated parameters have small biases. Consequently, we propose to use robust procedures. In particular, a simple robust estimator of the parameters is proposed and shown that its properties are comparable with other more complicated ones available in the literature. The properties of the estimated and predicted volatilities obtained by using robust filters based on robust parameter estimates are analyzed. All the results are illustrated using daily S&P500 and IBEX35 returns.

Keywords: Heteroscedasticity; M-estimator; QML estimator; Robustness; Financial Markets (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2008-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ivi:wpasad:2008-13

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