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Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall

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  • James W. Taylor
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    Abstract

    We propose exponentially weighted quantile regression (EWQR) for estimating time-varying quantiles. The EWQR cost function can be used as the basis for estimating the time-varying expected shortfall associated with the EWQR quantile forecast. We express EWQR in a kernel estimation framework, and then modify it by adapting a previously proposed double kernel estimator in order to provide greater accuracy for tail quantiles that are changing relatively quickly over time. We introduce double kernel quantile regression, which extends the double kernel idea to the modeling of quantiles in terms of regressors. In our empirical study of 10 stock returns series, the versions of the new methods that do not accommodate the leverage effect were able to outperform GARCH-based methods and CAViaR models. Copyright The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org, Oxford University Press.

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    Bibliographic Info

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

    Volume (Year): 6 (2008)
    Issue (Month): 3 (Summer)
    Pages: 382-406

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    Handle: RePEc:oup:jfinec:v:6:y:2008:i:3:p:382-406

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    Cited by:
    1. Shih-Kang Chao & Wolfgang Karl Härdle & Weining Wang, 2012. "Quantile Regression in Risk Calibration," SFB 649 Discussion Papers SFB649DP2012-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2013. "Financial network systemic risk contributions," CFS Working Paper Series 2013/20, Center for Financial Studies (CFS).
    3. Benjamin Hamidi & Emmanuel Jurczenko & Bertrand Maillet, 2009. "D'un multiple conditionnel en assurance de portefeuille : CAViaR pour les gestionnaires ?," Post-Print halshs-00389773, HAL.
    4. Thomas C. Chiang & Jiandong Li, 2012. "Stock Returns and Risk: Evidence from Quantile," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 5(1), pages 20-58, December.
    5. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
    6. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
    7. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2013. "Pair Copula Construction based Expected Shortfall estimation," Economics Bulletin, AccessEcon, vol. 33(2), pages 1067-1072.
    8. Dirk G Baur & Thomas Dimpfl, 2012. "State-dependent Momentum in International Stock Markets," Working Paper Series 169, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    9. Benjamin Hamidi & Bertrand Maillet & Jean-Luc Prigent, 2014. "A Dynamic AutoRegressive Expectile for Time-Invariant Portfolio Protection Strategies," Working Papers 2014-131, Department of Research, Ipag Business School.
    10. Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
    11. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
    12. repec:ecu:wpaper:2010-03 is not listed on IDEAS

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