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Modeling duration clusters with dynamic copulas

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  • Ng, Wing Lon

Abstract

This paper suggests a dynamic copula approach that allows more flexibility in capturing duration clusters of ultra-high frequent order book data. The proposed framework involves a time-varying mixing parameter and does not only model (a) the degree of dependence of consecutive durations, but also (b) the structure of (temporal) dependence of the duration process.

Suggested Citation

  • Ng, Wing Lon, 2008. "Modeling duration clusters with dynamic copulas," Finance Research Letters, Elsevier, vol. 5(2), pages 96-103, June.
  • Handle: RePEc:eee:finlet:v:5:y:2008:i:2:p:96-103
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    References listed on IDEAS

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    1. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation of copula-based semiparametric time series models," Journal of Econometrics, Elsevier, vol. 130(2), pages 307-335, February.
    2. 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.
    3. 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.
    4. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 125-154.
    5. Easley, David & O'Hara, Maureen, 1992. "Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
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    Cited by:

    1. Zhe Yan & Zhiping Chen & Giorgio Consigli & Jia Liu & Ming Jin, 2020. "A copula-based scenario tree generation algorithm for multiperiod portfolio selection problems," Annals of Operations Research, Springer, vol. 292(2), pages 849-881, September.
    2. Aepli, Matthias D. & Füss, Roland & Henriksen, Tom Erik S. & Paraschiv, Florentina, 2017. "Modeling the multivariate dynamic dependence structure of commodity futures portfolios," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 66-87.
    3. Haghighi, Afshin & Fallahpour, Saeid & Eyvazlu, Reza, 2016. "Modelling order arrivals at price limits using Hawkes processes," Finance Research Letters, Elsevier, vol. 19(C), pages 267-272.
    4. Kuang-Liang Chang, 2021. "A New Dynamic Mixture Copula Mechanism to Examine the Nonlinear and Asymmetric Tail Dependence Between Stock and Exchange Rate Returns," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 965-999, December.
    5. Aepli, Matthias D. & Frauendorfer, Karl & Fuess, Roland & Paraschiv, Florentina, 2015. "Multivariate Dynamic Copula Models: Parameter Estimation and Forecast Evaluation," Working Papers on Finance 1513, University of St. Gallen, School of Finance.
    6. Penikas, H., 2010. "Financial Applications of Copula-Models," Journal of the New Economic Association, New Economic Association, issue 7, pages 24-44.

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