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Spurious And Hidden Volatility

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Author Info
M. Angeles Carnero ()
Daniel Peña ()
Esther Ruiz ()

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Abstract

This paper analyses the effects caused by outliers on the identification and estimation of GARCH models. We show that outliers can lead to detect spurious conditional heteroscedasticity and can also hide genuine ARCH effects. First, we derive the asymptotic biases caused by outliers on the sample autocorrelations of squared observations and their effects on some homoscedasticity tests. Then, we obtain the asymptotic biases of the OLS estimates of ARCH(p) models and analyze their finite sample behavior by means of extensive Monte Carlo experiments. The finite sample results are extended to GLS and ML estimates of ARCH(p) and GARCH(1,1) models.

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Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws042007.

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Date of creation: Jul 2004
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Handle: RePEc:cte:wsrepe:ws042007

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  1. Pena D. & Rodriguez J., 2002. "A Powerful Portmanteau Test of Lack of Fit for Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 601-610, June. [Downloadable!] (restricted)
  2. Arup Bose & Kanchan Mukherjee, 2003. "Estimating The Arch Parameters By Solving Linear Equations," Journal of Time Series Analysis, Blackwell Publishing, vol. 24(2), pages 127-136, 03. [Downloadable!] (restricted)
  3. Xibin Zhang, 2004. "Assessment of Local Influence in GARCH Processes," Journal of Time Series Analysis, Blackwell Publishing, vol. 25(2), pages 301-313, 03. [Downloadable!] (restricted)
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