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Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2014-012
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Conditional quantile
kernel estimate
quantile autoregression
time series
uniform consistency
value-at-risk
JEL: 
C00
C14
C50
C58
Dokumentart: 
Working Paper

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