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Modelling conditional correlations of asset returns: A smooth transition approach

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Author Info

  • Annastiina Silvennoinen

    ()
    (School of Economics and Finance)

  • Timo Teräsvirta

    ()
    (Aarhus University, School of Economics and Management and CREATES)

Abstract

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM-test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the S&P 500 stock index completes the paper.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-09.

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Length: 34
Date of creation: 02 2012
Date of revision:
Handle: RePEc:aah:create:2012-09

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: GARCH; Constant conditional correlation; Dynamic conditional correlation; Return comovement; Variable correlation GARCH model; Volatility model evaluation;

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References

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  1. Robert-Paul Berben & W. Jos Jansen, 2003. "Comovement in international equity markets: A sectoral view," Finance 0310001, EconWPA.
  2. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
  3. Tse, Y. K., 2000. "A test for constant correlations in a multivariate GARCH model," Journal of Econometrics, Elsevier, vol. 98(1), pages 107-127, September.
  4. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
  5. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
  6. Mika Meitz & Pentti Saikkonen, 2008. "Parameter estimation in nonlinear AR-GARCH models," CREATES Research Papers 2008-30, School of Economics and Management, University of Aarhus.
  7. Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Multivariate GARCH models," CREATES Research Papers 2008-06, School of Economics and Management, University of Aarhus.
  8. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(02), pages 280-310, April.
  9. Susan Thorp & George Milunovich, 2007. "Symmetric Versus Asymmetric Conditional Covariance Forecasts: Does It Pay To Switch?," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 30(3), pages 355-377.
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Citations

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Cited by:
  1. L. Bauwens & E. Otranto, 2013. "Modeling the Dependence of Conditional Correlations on Volatility," Working Paper CRENoS 201304, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  2. repec:qut:auncer:2013_03 is not listed on IDEAS
  3. bauwens, Luc & hafner, Christian & pierret, Diane, 2011. "Multivariate volatility modeling of electricity futures," CORE Discussion Papers 2011011, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Heejoon Han & Dennis Kristensen, 2012. "Asymptotic Theory for the QMLE in GARCH-X Models with Stationary and Non-Stationary Covariates," CREATES Research Papers 2012-25, School of Economics and Management, University of Aarhus.
  5. Annastiina Silvennoinen & Susan Thorp, 2010. "Financialization, Crisis and Commodity Correlation Dynamics," Research Paper Series 267, Quantitative Finance Research Centre, University of Technology, Sydney.

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