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Dynamic Asymmetric Leverage in Stochastic Volatility Models

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Author Info

  • Manabu Asai
  • Michael McAleer

Abstract

In the class of stochastic volatility (SV) models, leverage effects are typically specified through the direct correlation between the innovations in both returns and volatility, resulting in the dynamic leverage (DL) model. Recently, two asymmetric SV models based on threshold effects have been proposed in the literature. As such models consider only the sign of the previous return and neglect its magnitude, this paper proposes a dynamic asymmetric leverage (DAL) model that accommodates the direct correlation as well as the sign and magnitude of the threshold effects. A special case of the DAL model with zero direct correlation between the innovations is the asymmetric leverage (AL) model. The dynamic asymmetric leverage models are estimated by the Monte Carlo likelihood (MCL) method. Monte Carlo experiments are presented to examine the finite sample properties of the estimator. For a sample size of T�=�2000 with 500 replications, the sample means, standard deviations, and root mean squared errors of the MCL estimators indicate only a small finite sample bias. The empirical estimates for S&P 500 and TOPIX financial returns, and USD/AUD and YEN/USD exchange rates, indicate that the DAL class, including the DL and AL models, is generally superior to threshold SV models with respect to AIC and BIC, with AL typically providing the best fit to the data.

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Bibliographic Info

Article provided by Taylor and Francis Journals in its journal Econometric Reviews.

Volume (Year): 24 (2005)
Issue (Month): 3 ()
Pages: 317-332

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Handle: RePEc:taf:emetrv:v:24:y:2005:i:3:p:317-332

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Related research

Keywords: Asymmetric effects; Monte Carlo likelihood; Stochastic volatility; Threshold effects;

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Cited by:
  1. Michael McAller & Marcelo C. Medeiros, 2007. "A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries," Textos para discussão 544, Department of Economics PUC-Rio (Brazil).
  2. Manabu Asai & Michael McAleer, 2005. "Asymmetric Multivariate Stochastic Volatility," DEA Working Papers 12, Universitat de les Illes Balears, Departament d'Economía Aplicada.
  3. Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility," Microeconomics Working Papers 22058, East Asian Bureau of Economic Research.
  4. George Kapetanios & Elias Tzavalis, 2006. "Stochastic Volatility Driven by Large Shocks," Working Papers 568, Queen Mary, University of London, School of Economics and Finance.
  5. Jerzy P. Rydlewski & Ma{\l}gorzata Snarska, 2012. "On Geometric Ergodicity of Skewed - SVCHARME models," Papers 1209.1544, arXiv.org.
  6. Manabu Asai & Michael McAleer & Marcelo C. Medeiros, 2010. "Asymmetry and Long Memory in Volatility Modelling," Working Papers in Economics 10/60, University of Canterbury, Department of Economics and Finance.
  7. McAleer, M.J., 2008. "The ten commandments for optimizing value-at-risk and daily capital charges," Econometric Institute Report EI 2008-32, Erasmus University Rotterdam, Econometric Institute.
  8. Tsyplakov, Alexander, 2010. "Revealing the arcane: an introduction to the art of stochastic volatility models," MPRA Paper 25511, University Library of Munich, Germany.
  9. repec:ebl:ecbull:v:3:y:2006:i:15:p:1-14 is not listed on IDEAS
  10. Fulvia Focker & Umberto Triacca, 2006. "A new proxy of the average volatility of a basket of returns: A Monte Carlo study," Economics Bulletin, AccessEcon, vol. 3(15), pages 1-14.
  11. Asai, Manabu, 2008. "Autoregressive stochastic volatility models with heavy-tailed distributions: A comparison with multifactor volatility models," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 332-341, March.
  12. Tsiakas, Ilias, 2008. "Overnight information and stochastic volatility: A study of European and US stock exchanges," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 251-268, February.

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