Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/93220
Authors: 
Franke, Jürgen
Mwita, Peter
Wang, Weining
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-012
Abstract: 
We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function, and we prove its asymptotic normality and uni- form strong consistency. The performance of the estimate for light and heavy-tailed distributions of the innovations are evaluated by a simulation study. Finally, the technique is applied to estimate VaR of stocks in DAX, and its performance is compared with the existing standard methods using backtesting.
Subjects: 
Conditional quantile
kernel estimate
quantile autoregression
time series
uniform consistency
value-at-risk
JEL: 
C00
C14
C50
C58
Document Type: 
Working Paper

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