IDEAS home Printed from https://ideas.repec.org/p/cor/louvco/2013014.html
   My bibliography  Save this paper

Modeling the dependence of conditional correlations on volatility

Author

Listed:
  • BAUWENS, Luc

    () (Université catholique de Louvain, CORE, B-1348 Louvain-la-Neuve, Belgium)

  • otranto, EDOARDO

    () (University of Messina, Italy)

Abstract

Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns, but few studies have investigated the determinants of the correlation dynamics. A common opinion is that the market volatility is a major determinant of the correlations. We extend some models to capture explicitly the dependence of the correlations on the volatility of the market of interest. The models differ in the way by which the volatility influences the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects. They are applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.

Suggested Citation

  • BAUWENS, Luc & otranto, EDOARDO, 2013. "Modeling the dependence of conditional correlations on volatility," CORE Discussion Papers 2013014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2013014
    as

    Download full text from publisher

    File URL: http://uclouvain.be/cps/ucl/doc/core/documents/coredp2013_14.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2013. "Multivariate Volatility Modeling Of Electricity Futures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 743-761, August.
    2. Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990. "Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 213-237.
    3. Luc Bauwens & Arie Preminger & Jeroen V. K. Rombouts, 2010. "Theory and inference for a Markov switching GARCH model," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 218-244, July.
    4. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    5. Annastiina Silvennoinen & Timo Teräsvirta, 2015. "Modeling Conditional Correlations of Asset Returns: A Smooth Transition Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 174-197, February.
    6. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    7. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
    8. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    9. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    10. Adam Clements & Mark Doolan & Stan Hurn & Ralf Becker, 2009. "Evaluating multivariate volatility forecasts," NCER Working Paper Series 41, National Centre for Econometric Research, revised 25 Nov 2009.
    11. Edoardo Otranto, 2005. "The multi-chain Markov switching model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 523-537.
    12. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    14. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    15. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    16. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    17. Bayoumi, Tamim & Fazio, Giorgio & Kumar, Manmohan & MacDonald, Ronald, 2007. "Fatal attraction: Using distance to measure contagion in good times as well as bad," Review of Financial Economics, Elsevier, vol. 16(3), pages 259-273.
    18. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    19. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. François Maniquet & Massimo Morelli, 2015. "Approval quorums dominate participation quorums," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 45(1), pages 1-27, June.
    2. Jean-David Fermanian & Hassan Malongo, 2013. "On the Stationarity of Dynamic Conditional Correlation Models," Working Papers 2013-26, Center for Research in Economics and Statistics.
    3. Edoardo Otranto & Massimo Mucciardi & Pietro Bertuccelli, 2016. "Spatial effects in dynamic conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 604-626, March.
    4. NESTEROV, Yurii, 2013. "Universal gradient methods for convex optimization problems," CORE Discussion Papers 2013026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Jean J. Gabszewicz & Skerdilajda Zanaj, 2015. "(Un)stable vertical collusive agreements," Canadian Journal of Economics, Canadian Economics Association, vol. 48(3), pages 924-939, August.
    6. repec:eee:intfor:v:34:y:2018:i:1:p:45-63 is not listed on IDEAS
    7. Ana Mauleon & Elena Molis & Vincent Vannetelbosch & Wouter Vergote, 2014. "Dominance invariant one-to-one matching problems," International Journal of Game Theory, Springer;Game Theory Society, vol. 43(4), pages 925-943, November.
    8. Dudek, Jérémy, 2013. "Illiquidité, contagion et risque systémique," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/13236 edited by Le Fol, Gaëlle, January.
    9. CORNUEJOLS, Gérard & WOLSEY, Laurence & YILDIZ, Sercan, 2013. "Sufficiency of cut-generating functions," CORE Discussion Papers 2013027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    11. Becker, Christoph & Schmidt, Wolfgang M., 2015. "How past market movements affect correlation and volatility," Journal of International Money and Finance, Elsevier, vol. 50(C), pages 78-107.

    More about this item

    Keywords

    volatility effects; conditional correlation; DCC; Markov switching;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cor:louvco:2013014. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alain GILLIS). General contact details of provider: http://edirc.repec.org/data/coreebe.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.