IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v12y2014i4p620-644..html
   My bibliography  Save this article

Conditional Skewness with Quantile Regression Models: SoFiE Presidential Address and a Tribute to Hal White

Author

Listed:
  • Eric Ghysels

Abstract

We study a new class of conditional skewness models based on conditional quantiles regressions. The approach is much inspired by work of Hal White. To handle multiple horizons I consider quantile MIDAS regressions which amount to direct forecasting—as opposed to iterated forecasting—conditional skewness. Using this quantile-based approach I document that the conditional asymmetry of returns varies significantly over time. The asymmetry is most relevant for the characterization of downside risk. Besides empirical evidence, I also report simulation results which highlight the costs associated with mis-specifying downside risk in the presence of conditional skewness.

Suggested Citation

  • Eric Ghysels, 2014. "Conditional Skewness with Quantile Regression Models: SoFiE Presidential Address and a Tribute to Hal White," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 620-644.
    • Handle: RePEc:oup:jfinec:v:12:y:2014:i:4:p:620-644.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbu021
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    2. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    3. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    4. Nina Boyarchenko & Domenico Giannone & Or Shachar, 2018. "Flighty liquidity," Staff Reports 870, Federal Reserve Bank of New York.
    5. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    6. Riccardo Colacito & Eric Ghysels & Jinghan Meng & Wasin Siwasarit, 2016. "Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory," Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2069-2109.
    7. Nicholas Apergis, 2022. "Evaluating tail risks for the U.S. economic policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3971-3989, October.
    8. Sulkhan Chavleishvili & Simone Manganelli, 2024. "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 66-85, January.
    9. Shuting Liu & Qifa Xu & Cuixia Jiang, 2021. "Systemic risk of China’s commercial banks during financial turmoils in 2010-2020: A MIDAS-QR based CoVaR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 28(18), pages 1600-1609, October.
    10. Iania, Leonardo & Algieri, Bernardina & Leccadito, Arturo, 2022. "Forecasting total energy’s CO2 emissions," LIDAM Discussion Papers LFIN 2022003, Université catholique de Louvain, Louvain Finance (LFIN).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:jfinec:v:12:y:2014:i:4:p:620-644.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.