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Weighted Nadaraya--Watson Estimation of Conditional Expected Shortfall

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  • Kengo Kato
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    Abstract

    This paper addresses the problem of nonparametric estimation of the conditional expected shortfall (CES) that has gained popularity in financial risk management. We propose a new nonparametric estimator of the CES. The proposed estimator is defined as a conditional counterpart of the sample average estimator of the unconditional expected shortfall, where the empirical distribution function is replaced by the weighted Nadaraya--Watson estimator of the conditional distribution function. We establish asymptotic normality of the proposed estimator under an α-mixing condition. The asymptotic results reveal that the proposed estimator has a good bias property. Simulation results illustrate the usefulness of the proposed estimator. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbs002
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    Bibliographic Info

    Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

    Volume (Year): 10 (2012)
    Issue (Month): 2 (2012 15)
    Pages: 265-291

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    Handle: RePEc:oup:jfinec:v:10:y:2012:i:2:p:265-291

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    Cited by:
    1. PIERRET, Diane, 2013. "The systemic risk of energy markets," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2013018, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Rockafellar, R.T. & Royset, J.O. & Miranda, S.I., 2014. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk," European Journal of Operational Research, Elsevier, Elsevier, vol. 234(1), pages 140-154.

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