Estimation and Inference in ARCH Models in the Presence of Outliers
AbstractIn this paper, we show the effects that outliers have on estimation and inference for autoregressive conditional heteroskedasticity (ARCH) models. We propose for a wide class of ARCH models commonly estimated, an empirically tractable solution to this problem by replacing outliers with their conditional expectations (optimal forecasts) in the likelihood function. This solution works well in both simulations and applications, as opposed to dummy variables which can lead to multimodality in the ARCH likelihood and invalid inference. We demonstrate the accuracy of our procedure for parameter estimation and forecasting. The empirical examples include U.S. interest rate, foreign exchange rate, and stock index data. In addition, we suggest a robust bootstrap test for outliers and evaluate this against the Andrews (2003) S test. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org, Oxford University Press.
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Bibliographic InfoArticle provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.
Volume (Year): 8 (2010)
Issue (Month): 4 (Fall)
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- M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2004.
"Spurious And Hidden Volatility,"
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- Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
- Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Working Papers 05-44, Bank of Canada.
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