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Kernel Conditional Quantile Estimation for Stationary Processes with Application to Conditional Value-at-Risk

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  • Wei Biao Wu
  • Keming Yu
  • Gautam Mitra

Abstract

The paper considers kernel estimation of conditional quantiles for both short-range and long-range-dependent processes. Under mild regularity conditions, we obtain Bahadur representations and central limit theorems for kernel quantile estimates of those processes. Our theory is applicable to many price processes of assets in finance. In particular, we present an asymptotic theory for kernel estimates of the value-at-risk (VaR) of the market value of an asset conditional on the historical information or a state process. The results are assessed based on a small simulation and are applied to AT&T monthly returns. Copyright The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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  • Wei Biao Wu & Keming Yu & Gautam Mitra, 2008. "Kernel Conditional Quantile Estimation for Stationary Processes with Application to Conditional Value-at-Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(2), pages 253-270, Spring.
  • Handle: RePEc:oup:jfinec:v:6:y:2008:i:2:p:253-270
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbm022
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    Cited by:

    1. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
    2. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org.

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