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    Nonparametric Estimates for Conditional Quantiles of Time Series

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    Date
    2014
    Author
    Franke, Jürgen
    Mwita, Peter N.
    Wang, Weining
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    Abstract
    We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt , where Xt can either 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 uniform 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.
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    http://ir.mksu.ac.ke/handle/123456780/4735
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    • School of Pure and Applied Sciences [259]

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