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Estimating dynamic copula dependence using intraday data

Author

Listed:
  • Grossmass Lidan

    (Department of Economics, University of Konstanz, Box 124, 78457 Konstanz, Germany)

  • Poon Ser-Huang

    (Manchester Business School, Crawford House, University of Manchester, Manchester, UK)

Abstract

We estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

Suggested Citation

  • Grossmass Lidan & Poon Ser-Huang, 2015. "Estimating dynamic copula dependence using intraday data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 501-529, September.
    • Handle: RePEc:bpj:sndecm:v:19:y:2015:i:4:p:501-529:n:4
    DOI: 10.1515/snde-2013-0123
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    References listed on IDEAS

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    1. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
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    Citations

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    Cited by:

    1. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    2. Jiang, Cuixia & Ding, Xiaoyi & Xu, Qifa & Tong, Yongbo, 2020. "A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    3. Arnab Chakrabarti & Rituparna Sen, 2019. "Copula estimation for nonsynchronous financial data," Papers 1904.10182, arXiv.org, revised Sep 2020.

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    More about this item

    Keywords

    copula; high frequency data; intraday dependence; time-varying dependence; value-at-risk;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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