Nonparametric estimates for conditional quantiles of time series

Jürgen Franke, Peter Mwita and Weining Wang

No 2014-012, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: Conditional quantile; kernel estimate; quantile autoregression; time series; uniform consistency; value-at-risk (search for similar items in EconPapers)
JEL-codes: C00 C14 C50 C58 (search for similar items in EconPapers)
Date: 2014
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Journal Article: Nonparametric estimates for conditional quantiles of time series (2015)
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