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Nonlinearities and regimes in conditional correlations with different dynamics

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  • BAUWENS Luc,

    (Université catholique de Louvain, CORE, Belgium)

  • OTRANTO Edoardo,

    (Universita ́ di Messina)

Abstract

New parameterizations of the dynamic conditional correlation (DCC) model and of the regime-switching dynamic correlation (RSDC) model are introduced, such that these models provide a specific dynamics for each correlation. They imply a non- linear autoregressive form of dependence on lagged correlations and are based on properties of the Hadamard exponential matrix. The new models are applied to a data set of twenty stock market indices, comparing them to the classical DCC and RSDC models. The empirical results show that the new models improve their clas- sical versions in terms of several criteria.

Suggested Citation

  • BAUWENS Luc, & OTRANTO Edoardo,, 2018. "Nonlinearities and regimes in conditional correlations with different dynamics," LIDAM Discussion Papers CORE 2018009, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:2018009
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    1. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    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. Bauwens, Luc & Grigoryeva, Lyudmila & Ortega, Juan-Pablo, 2016. "Estimation and empirical performance of non-scalar dynamic conditional correlation models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 17-36.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    6. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    7. Giampiero M. Gallo & Edoardo Otranto, 2018. "Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 549-573, April.
    8. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2019. "Volatility-dependent correlations: further evidence of when, where and how," Empirical Economics, Springer, vol. 57(2), pages 505-540, August.
    9. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    10. Luc Bauwens & Edoardo Otranto, 2016. "Modeling the Dependence of Conditional Correlations on Market Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 254-268, April.
    11. 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.
    12. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    13. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    14. Gallo, Giampiero M. & Otranto, Edoardo, 2015. "Forecasting realized volatility with changing average levels," International Journal of Forecasting, Elsevier, vol. 31(3), pages 620-634.
    15. Annastiina Silvennoinen & Timo Ter�svirta, 2015. "Modeling Conditional Correlations of Asset Returns: A Smooth Transition Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 174-197, February.
    16. Monica Billio & Massimiliano Caporin, 2005. "Multivariate Markov switching dynamic conditional correlation GARCH representations for contagion analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 14(2), pages 145-161, November.
    17. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    18. Hsiang‐Tai Lee & Jonathan Yoder, 2007. "Optimal hedging with a regime‐switching time‐varying correlation GARCH model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(5), pages 495-516, May.
    19. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    20. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    21. 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.
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    Cited by:

    1. Luc Bauwens & Edoardo Otranto, 2023. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1376-1401.
    2. Bauwens, Luc & Otranto, Edoardo, 2023. "Realized Covariance Models with Time-varying Parameters and Spillover Effects," LIDAM Discussion Papers CORE 2023019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    4. Mariagrazia Fallanca & Antonio Fabio Forgione & Edoardo Otranto, 2021. "Do the Determinants of Non-Performing Loans Have a Different Effect over Time? A Conditional Correlation Approach," JRFM, MDPI, vol. 14(1), pages 1-15, January.

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    More about this item

    Keywords

    dynamic conditional correlations; regime-switching dynamic correla- tions; Hadamard exponential matrix;
    All these keywords.

    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

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