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Time-Varying Mixture Copula Models with Copula Selection

Bingduo Yang, Zongwu Cai, Christian Hafner () and Guannan Liu
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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
Guannan Liu: School of Economics and WISE, Xiamen University, Xiamen, Fujian 361005, China

No 202105, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models for dependence often encounter a lack of fit in the joint tails, calling for additional flexibility. This paper introduces a new semiparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic and unspecified functions of time. We propose penalized time-varying mixture copula models with group smoothly clipped absolute deviation penalty functions to do the estimation and copula selection simultaneously. Monte Carlo simulation results suggest that the shrinkage estimation procedure performs well in selecting and estimating both constant and time-varying mixture copula models. Using the proposed model and method, we analyze the evolution of the dependence among four international stock markets, and find substantial changes in the levels and patterns of the dependence, in particular around crisis periods.

Keywords: Copula Selection; EM Algorithm; Mixture Copula; SCAD; Time-Varying Distribution. (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, nep-ets, nep-ore and nep-rmg
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Related works:
Working Paper: Time-Varying Mixture Copula Models with Copula Selection (2019)
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