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Semiparametric estimation and model selection for conditional mixture copula models

Author

Listed:
  • Guannan Liu
  • Wei Long
  • Bingduo Yang
  • Zongwu Cai
Abstract
Conditional copula models allow the dependence structure among variables to vary with covariates, and thus can describe the evolution of the dependence structure with those factors. This paper proposes a conditional mixture copula which is a weighted average of several individual conditional copulas. We allow both the weights and copula parameters to vary with a covariate so that the conditional mixture copula offers additional flexibility and accuracy in describing the dependence structure. We propose a two‐step semi‐parametric estimation method and develop asymptotic properties of the estimators. Moreover, we introduce model selection procedures to select the component copulas of the conditional mixture copula model. Simulation results suggest that the proposed procedures have a good performance in estimating and selecting conditional mixture copulas with different model specifications. The proposed model is then applied to investigate how the dependence structures among international equity markets evolve with the volatility in the exchange rate markets.

Suggested Citation

  • Guannan Liu & Wei Long & Bingduo Yang & Zongwu Cai, 2022. "Semiparametric estimation and model selection for conditional mixture copula models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 287-330, March.
    • Handle: RePEc:bla:scjsta:v:49:y:2022:i:1:p:287-330
    DOI: 10.1111/sjos.12514
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    References listed on IDEAS

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    Cited by:

    1. Zongwu Cai & Guannan Liu & Wei Long & Xuelong Luo, 2024. "Semiparametric Conditional Mixture Copula Models with Copula Selection," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202401, University of Kansas, Department of Economics, revised Jan 2024.

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