英语翻译Parametric copulas are shown to be attractive devices for specifyingquantile autoregressive models for nonlinear time-series.Estimation of local,quantile-specific copula-based time series models offers some salientadvantages over classica

英语翻译Parametric copulas are shown to be attractive devices for specifyingquantile autoregressive models for nonlinear time-series.Estimation of local,quantile-specific copula-based time series models offers some salientadvantages over classica
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.

英语翻译Parametric copulas are shown to be attractive devices for specifyingquantile autoregressive models for nonlinear time-series.Estimation of local,quantile-specific copula-based time series models offers some salientadvantages over classica
参数Copula函数的证明是指定位数自回归模型的非线性时间序列吸引力的设备.地方,位数,具体系词的时间序列模型估算全球提供超过传统的参数化方法的一些突出的优势.一致性和建议的分位数估计的渐近正态性温和的条件下成立,
让全球误设的参数Copula函数和边缘人,没有担任任何混合率的条件.这些结果导致对推理和示范价值的极端条件测试规范的总体框架,在为金融时间序列数据的风险.