IDEAS home Printed from https://ideas.repec.org/p/aiz/louvar/2010033.html
   My bibliography  Save this paper

Efficient estimation of a semiparametric dynamic copula model

Author

Listed:
  • Hafner, Christian
  • Reznikova, Olga

Abstract

A new semiparametric dynamic copula model is proposed where the marginals are specified as parametric GARCH-type processes, and the dependence parameter of the copula is allowed to change over time in a nonparametric way. A straightforward two-stage estimation method is given by local maximum likelihood for the dependence parameter, conditional on consistent first stage estimates of the marginals. First, the properties of the estimator are characterized in terms of bias and variance and the bandwidth selection problem is discussed. The proposed estimator attains the semiparametric efficiency bound and its superiority is demonstrated through simulations. Finally, the wide applicability of the model in financial time series is illustrated, and it is compared with traditional models based on conditional correlations.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Hafner, Christian & Reznikova, Olga, 2010. "Efficient estimation of a semiparametric dynamic copula model," LIDAM Reprints ISBA 2010033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2010033
    Note: In : Computational Statistics and Data Analysis, vol. 54, no. 11, p. 2609-2627 (2010)
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
    2. 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.
    3. Drost, F.C. & Klaassen, C.A.J. & Werker, B.J.M., 1994. "Adaptive estimation in time-series models," Discussion Paper 1994-88, Tilburg University, Center for Economic Research.
    4. Song, Peter X.K. & Fan, Yanqin & Kalbfleisch, John D., 2005. "Maximization by Parts in Likelihood Inference," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1145-1158, December.
    5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    6. J. Fan & M. Farmen & I. Gijbels, 1998. "Local maximum likelihood estimation and inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 591-608.
    7. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    8. Chiang, Thomas C. & Jeon, Bang Nam & Li, Huimin, 2007. "Dynamic correlation analysis of financial contagion: Evidence from Asian markets," Journal of International Money and Finance, Elsevier, vol. 26(7), pages 1206-1228, November.
    9. Fernandez, Viviana, 2006. "The impact of major global events on volatility shifts: Evidence from the Asian crisis and 9/11," Economic Systems, Elsevier, vol. 30(1), pages 79-97, March.
    10. Dobric, Jadran & Schmid, Friedrich, 2007. "A goodness of fit test for copulas based on Rosenblatt's transformation," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4633-4642, May.
    11. 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.
    12. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leonidas Tsiaras, 2010. "Dynamic Models of Exchange Rate Dependence Using Option Prices and Historical Returns," CREATES Research Papers 2010-35, Department of Economics and Business Economics, Aarhus University.
    2. Arthur Charpentier, 2015. "Prévision avec des copules en finance," Working Papers hal-01151233, HAL.
    3. Avdulaj, Krenar & Barunik, Jozef, 2015. "Are benefits from oil–stocks diversification gone? New evidence from a dynamic copula and high frequency data," Energy Economics, Elsevier, vol. 51(C), pages 31-44.
    4. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    5. R. Khalfaoui & M. Boutahar, 2012. "Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," Working Papers halshs-00793068, HAL.
    6. Chopra, Monika & Mehta, Chhavi, 2022. "Is the COVID-19 pandemic more contagious for the Asian stock markets? A comparison with the Asian financial, the US subprime and the Eurozone debt crisis," Journal of Asian Economics, Elsevier, vol. 79(C).
    7. Sercan Demiralay & Veysel Ulusoy, 2017. "How Has the Behavior of Cross-Market Correlations Altered During Financial and Debt Crises?," Manchester School, University of Manchester, vol. 85(6), pages 765-794, December.
    8. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    9. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.
    10. Hafner, Christian M. & Linton, Oliver, 2010. "Efficient estimation of a multivariate multiplicative volatility model," Journal of Econometrics, Elsevier, vol. 159(1), pages 55-73, November.
    11. Guoxiang Xu & Wangfeng Gao, 2019. "Financial Risk Contagion in Stock Markets: Causality and Measurement Aspects," Sustainability, MDPI, vol. 11(5), pages 1-20, March.
    12. Zouheir Mighri, 2018. "On the Dynamic Linkages Among International Emerging Currencies," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 427-473, June.
    13. Rob van den Goorbergh, 2004. "A Copula-Based Autoregressive Conditional Dependence Model of International Stock Markets," DNB Working Papers 022, Netherlands Central Bank, Research Department.
    14. Das, Suman & Roy, Saikat Sinha, 2023. "Following the leaders? A study of co-movement and volatility spillover in BRICS currencies," Economic Systems, Elsevier, vol. 47(2).
    15. Sehgal, Sanjay & Pandey, Piyush & Diesting, Florent, 2017. "Examining dynamic currency linkages amongst South Asian economies: An empirical study," Research in International Business and Finance, Elsevier, vol. 42(C), pages 173-190.
    16. Silvennoinen, Annastiina & Teräsvirta, Timo, 2024. "Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model," Econometrics and Statistics, Elsevier, vol. 32(C), pages 57-72.
    17. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2014. "Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas," SIRE Discussion Papers 2015-25, Scottish Institute for Research in Economics (SIRE).
    18. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    19. Roberto Casarin & Marco Tronzano & Domenico Sartore, 2013. "Bayesian Markov Switching Stochastic Correlation Models," Working Papers 2013:11, Department of Economics, University of Venice "Ca' Foscari".
    20. Krenar AVDULAJ & Jozef BARUNIK, 2013. "Can We Still Benefit from International Diversification? The Case of the Czech and German Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(5), pages 425-442, November.

    More about this item

    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:aiz:louvar:2010033. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nadja Peiffer (email available below). General contact details of provider: https://edirc.repec.org/data/isuclbe.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.