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High dimensional dynamic stochastic copula models

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  • Creal, Drew D.
  • Tsay, Ruey S.

Abstract

We build a class of copula models that captures time-varying dependence across large panels of financial assets. Our models nest Gaussian, Student’s t, grouped Student’s t, and generalized hyperbolic copulas with time-varying correlations matrices, as special cases. We introduce time-variation into the densities by writing them as factor models with stochastic loadings. The proposed copula models have flexible dynamics and heavy tails yet remain tractable in high dimensions due to their factor structure. Our Bayesian estimation approach leverages a recent advance in sequential Monte Carlo methods known as particle Gibbs sampling which can draw large blocks of latent variables efficiently and in parallel. We use this framework to model an unbalanced, 200-dimensional panel consisting of credit default swaps and equities for 100 US corporations. Our analysis shows that the grouped Student’s t stochastic copula is preferred over seven competing models.

Suggested Citation

  • Creal, Drew D. & Tsay, Ruey S., 2015. "High dimensional dynamic stochastic copula models," Journal of Econometrics, Elsevier, vol. 189(2), pages 335-345.
  • Handle: RePEc:eee:econom:v:189:y:2015:i:2:p:335-345
    DOI: 10.1016/j.jeconom.2015.03.027
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    1. repec:taf:jnlasa:v:108:y:2013:i:502:p:656-665 is not listed on IDEAS
    2. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January.
    3. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    4. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    5. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    7. André Lucas & Bernd Schwaab & Xin Zhang, 2014. "Conditional Euro Area Sovereign Default Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 271-284, April.
    8. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    9. Peter Christoffersen & Kris Jacobs & Xisong Jin & Hugues Langlois, 2013. "Dynamic Diversification in Corporate Credit," CREATES Research Papers 2013-46, Department of Economics and Business Economics, Aarhus University.
    10. Michael S. Smith & Quan Gan & Robert J. Kohn, 2012. "Modelling dependence using skew t copulas: Bayesian inference and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 500-522, April.
    11. Michael Pitt & David Chan & Robert Kohn, 2006. "Efficient Bayesian inference for Gaussian copula regression models," Biometrika, Biometrika Trust, vol. 93(3), pages 537-554, September.
    12. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    13. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    14. Nicolas Chopin & Sumeetpal S. Singh, 2013. "On the Particle Gibbs Sampler," Working Papers 2013-41, Center for Research in Economics and Statistics.
    15. Christian M. Hafner & Hans Manner, 2012. "Dynamic stochastic copula models: estimation, inference and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 269-295, March.
    16. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October.
    17. 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.
    18. Hans Manner & Olga Reznikova, 2012. "A Survey on Time-Varying Copulas: Specification, Simulations, and Application," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 654-687, November.
    19. Drew D. Creal & Robert B. Gramacy & Ruey S. Tsay, 2014. "Market-Based Credit Ratings," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 430-444, July.
    20. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(5), pages 933-956, October.
    21. Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 511-546, Fall.
    22. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    23. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    24. Krupskii, Pavel & Joe, Harry, 2013. "Factor copula models for multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 85-101.
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    More about this item

    Keywords

    State space models; Dynamic copulas; Bayesian estimation; Particle filters; Credit default swaps;
    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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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