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Semiparametric Estimation and Model Selection for Conditional Mixture Copula Models

Author

Listed:
  • Guannan Liu

    (School of Economics and WISE, Xiamen University, Xiamen, Fujian 361005, China)

  • Wei Long

    (Department of Economics, Tulane University, New Orleans, LA 70118, USA)

  • Bingduo Yang

    (Lingnan (University) College, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China)

  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

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 semiparametric 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, 2021. "Semiparametric Estimation and Model Selection for Conditional Mixture Copula Models," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202104, University of Kansas, Department of Economics, revised Jan 2021.
    • Handle: RePEc:kan:wpaper:202104
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    File URL: http://www2.ku.edu/~kuwpaper/2021Papers/202104.pdf
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    References listed on IDEAS

    as
    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. Elif F. Acar & Radu V. Craiu & Fang Yao, 2011. "Dependence Calibration in Conditional Copulas: A Nonparametric Approach," Biometrics, The International Biometric Society, vol. 67(2), pages 445-453, June.
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    Cited by:

    1. Jing Yuan & Yajing Dong & Weijie Zhai & Zongwu Cai, 2021. "Economic Policy Uncertainty: Cross-Country Linkages and Spillover Effects on Economic Development in Some Belt and Road Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202110, University of Kansas, Department of Economics, revised Nov 2021.

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    More about this item

    Keywords

    Conditional copula; Mixture copula; Model selection; Semiparametric estimation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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