Estimation of copula-based semiparametric time series models |
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Authors: | Xiaohong Chen Yanqin Fan |
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Institution: | aDepartment of Economics, New York University, 269 Mercer Street, New York, NY 10003, USA;bDepartment of Economics, Vanderbilt University, VU Station B 351819, 2301 Vanderbilt Place, Nashville, TN 37235, USA |
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Abstract: | This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric marginal distributions and parametric copula functions, while the copulas capture all the scale-free temporal dependence of the processes. Simple estimators of the marginal distribution and the copula parameter are provided, and their asymptotic properties are established under easily verifiable conditions. These results are used to obtain root-n consistent and asymptotically normal estimators of important features of the transition distribution such as the (nonlinear) conditional moments and conditional quantiles. The semiparametric conditional quantile estimators are automatically monotonic across quantiles, which is attractive for portfolio conditional value-at-risk calculations. |
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Keywords: | Copula Nonlinear Markov models Semiparametric estimation Conditional moment Conditional quantile |
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