Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64801 
Year of Publication: 
2008
Series/Report no.: 
cemmap working paper No. CWP27/08
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Subjects: 
Quantile autoregression
Copula
Ergodic nonlinear Markov data
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
330.71 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.