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Stylized facts of return series, robust estimates and three popular models of volatility

  • Timo Terasvirta
  • Zhenfang Zhao

Financial return series of sufficiently high frequency display stylized facts such as volatility clustering, high kurtosis, low starting and slow-decaying autocorrelation function of squared returns and the so-called Taylor effect. In order to evaluate the capacity of volatility models to reproduce these facts, we apply both standard and robust measures of kurtosis and autocorrelation of squares to first-order Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential GARCH (EGARCH) and Autoregressive Stochastic Volaticity (ARSV) models. Robust measures provide a fresh view of stylized facts, which is useful because many financial time series can be viewed as being contaminated with outliers.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/09603107.2011.523195
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Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 21 (2011)
Issue (Month): 1-2 ()
Pages: 67-94

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Handle: RePEc:taf:apfiec:v:21:y:2011:i:1-2:p:67-94
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  1. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Evaluating GARCH models," SSE/EFI Working Paper Series in Economics and Finance 292, Stockholm School of Economics, revised 03 May 1999.
  2. Eklund, Bruno, 2005. "Estimating confidence regions over bounded domains," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 349-360, April.
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  9. He, Changli & Ter svirta, Timo & Malmsten, Hans, 2002. "Moment Structure Of A Family Of First-Order Exponential Garch Models," Econometric Theory, Cambridge University Press, vol. 18(04), pages 868-885, August.
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