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Semiparametric Estimation and Model Selection for Conditional Mixture Copula Models

Guannan Liu, Wei Long, Bingduo Yang and Zongwu Cai
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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

No 202104, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

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.

Keywords: Conditional copula; Mixture copula; Model selection; Semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2021-01, Revised 2021-01
New Economics Papers: this item is included in nep-ecm and nep-ore
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