Stylized facts of return series, robust estimates and three popular models of volatility
AbstractFinancial 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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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Financial Economics.
Volume (Year): 21 (2011)
Issue (Month): 1-2 ()
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Web page: http://www.tandfonline.com/RAFE20
Other versions of this item:
- Teräsvirta, Timo & Zhao, Zhenfang, 2007. "Stylized Facts of Return Series, Robust Estimates, and Three Popular Models of Volatility," Working Paper Series in Economics and Finance 662, Stockholm School of Economics, revised 05 Jun 2007.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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- Cristina Amado & Timo Teräsvirta, 2008.
"Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure,"
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NIPE Working Papers
01/2011, NIPE - Universidade do Minho.
- Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014.
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Documents de travail du Centre d'Economie de la Sorbonne
14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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- Lorenzo Pascual & Esther Ruiz & Diego Fresoli, 2011. "Bootstrap forecast of multivariate VAR models without using the backward representation," Statistics and Econometrics Working Papers ws113426, Universidad Carlos III, Departamento de Estadística y Econometría.
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