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Stochastic volatility models: conditional normality versus heavy-tailed distributions

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Author Info
Roman Liesenfeld (Eberhard-Karls-Universitat Tubingen, Department of Economics, Mohlstrasse 36, 72074 Tubingen, Germany)
Robert C. Jung (Eberhard-Karls-Universitat Tubingen, Department of Economics, Mohlstrasse 36, 72074 Tubingen, Germany)

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Abstract

Most of the empirical applications of the stochastic volatility (SV) model are based on the assumption that the conditional distribution of returns, given the latent volatility process, is normal. In this paper, the SV model based on a conditional normal distribution is compared with SV specifications using conditional heavy-tailed distributions, especially Student's t-distribution and the generalized error distribution. To estimate the SV specifications, a simulated maximum likelihood approach is applied. The results based on daily data on exchange rates and stock returns reveal that the SV model with a conditional normal distribution does not adequately account for the two following empirical facts simultaneously: the leptokurtic distribution of the returns and the low but slowly decaying autocorrelation functions of the squared returns. It is shown that these empirical facts are more adequately captured by an SV model with a conditional heavy-tailed distribution. It also turns out that the choice of the conditional distribution has systematic effects on the parameter estimates of the volatility process. Copyright © 2000 John Wiley & Sons, Ltd.Journal: Journal of Applied Econometrics

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 15 (2000)
Issue (Month): 2 ()
Pages: 137-160
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Handle: RePEc:jae:japmet:v:15:y:2000:i:2:p:137-160

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Adam Clements & Stan Hurn & Scott White, 2006. "Estimating Stochastic Volatility Models Using a Discrete Non-linear Filter. Working paper #3," NCER Working Paper Series 3, National Centre for Econometric Research. [Downloadable!]
  2. PREMINGER, Arie & HAFNER, Christian M., 2006. "Deciding between GARCH and stochastic volatility via strong decision rules," CORE Discussion Papers 2006042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  3. Manabu Asai & Michael McAleer, 2005. "Asymmetric Multivariate Stochastic Volatility," DEA Working Papers 12, Universitat de les Illes Balears, Departament d'Economía Aplicada. [Downloadable!]
  4. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004. [Downloadable!]
  5. Mark J. Jensen & John M. Maheu, 2008. "Bayesian semiparametric stochastic volatility modeling," Working Paper 2008-15, Federal Reserve Bank of Atlanta. [Downloadable!]
    Other versions:
  6. Jung, Robert & Liesenfeld, Roman & Richard, Jean-Francois, 2008. "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Economics Working Papers 2008,12, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
  7. Fulvia Focker & Umberto Triacca, 2006. "A new proxy of the average volatility of a basket of returns: A Monte Carlo study," Economics Bulletin, Economics Bulletin, vol. 3(15), pages 1-14. [Downloadable!]
  8. Carmen Broto & Esther Ruiz, 2002. "Estimation Methods For Stochastic Volatility Models: A Survey," Statistics and Econometrics Working Papers ws025414, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
    Other versions:
  9. Adam Clements & Scott White, 2005. "Nonlinear Filtering for Stochastic Volatility Models with Heavy Tails and Leverage," School of Economics and Finance Discussion Papers and Working Papers Series 192, School of Economics and Finance, Queensland University of Technology. [Downloadable!]
  10. Nikolaus Hautsch & Yangguoyi Ou, 2008. "Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference," SFB 649 Discussion Papers SFB649DP2008-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  11. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 7, Università di Verona, Dipartimento di Scienze economiche. [Downloadable!]
    Other versions:
  12. Esther Ruiz & Helena Veiga, 2006. "Modelling Long-Memory Volatilities With Leverage Effect: Almsv Versus Fiegarch," Statistics and Econometrics Working Papers ws066016, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
    Other versions:
  13. Jonathan Wright, 2002. "Log-Periodogram Estimation Of Long Memory Volatility Dependencies With Conditionally Heavy Tailed Returns," Econometric Reviews, Taylor and Francis Journals, vol. 21(4), pages 397-417. [Downloadable!] (restricted)
    Other versions:
  14. Mohammed Bouaddi & Jeroen V.K. Rombouts, 2007. "Mixed Exponential Power Asymmetric Conditional Heteroskedasticity," Cahiers de recherche 0749, CIRPEE. [Downloadable!]
    Other versions:
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