英语翻译Parametric copulas are shown to be attractive devices fo

英语翻译
Parametric copulas are shown to be attractive devices for specifying
quantile autoregressive models for nonlinear time-series.Estimation of local,
quantile-specific copula-based time series models offers some salient
advantages over classical global parametric approaches.Consistency and
asymptotic normality of the proposed quantile estimators are established
under mild conditions,allowing for global misspecification of parametric
copulas and marginals,and without assuming any mixing rate condition.
These results lead to a general framework for inference and model specification
testing of extreme conditional value-at-risk for financial time series
data.
Estimation of models for conditional quantiles constitutes an essential ingredient
in modern risk assessment.And yet,often,such quantile estimation and prediction
rely heavily on unrealistic global distributional assumptions.In this paper
we consider new estimation methods for conditional quantile functions that are
motivated by parametric copula models,but retain some semi-parametric flexibility
and thus,should deliver more robust and more accurate estimates,while also
being well-suited to the evaluation of misspecification.