IDEAS home Printed from https://ideas.repec.org/a/bla/jfinan/v74y2019i1p323-370.html
   My bibliography  Save this article

(Almost) Model‐Free Recovery

Author

Listed:
  • PAUL SCHNEIDER
  • FABIO TROJANI

Abstract

Under mild assumptions, we recover the model‐free conditional minimum variance projection of the pricing kernel on various tradeable realized moments of market returns. Recovered conditional moments predict future realizations and give insight into the cyclicality of equity premia, variance risk premia, and the highest attainable Sharpe ratios under the minimum variance probability. The pricing kernel projections are often U‐shaped and give rise to optimal conditional portfolio strategies with plausible market timing properties, moderate countercyclical exposures to higher realized moments, and favorable out‐of‐sample Sharpe ratios.

Suggested Citation

  • Paul Schneider & Fabio Trojani, 2019. "(Almost) Model‐Free Recovery," Journal of Finance, American Finance Association, vol. 74(1), pages 323-370, February.
  • Handle: RePEc:bla:jfinan:v:74:y:2019:i:1:p:323-370
    DOI: 10.1111/jofi.12737
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jofi.12737
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jofi.12737?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Fousseni Chabi-Yo & Chukwuma Dim & Grigory Vilkov, 2023. "Generalized Bounds on the Conditional Expected Excess Return on Individual Stocks," Management Science, INFORMS, vol. 69(2), pages 922-939, February.
    2. Can Gao & Ian W. R. Martin, 2021. "Volatility, Valuation Ratios, and Bubbles: An Empirical Measure of Market Sentiment," Journal of Finance, American Finance Association, vol. 76(6), pages 3211-3254, December.
    3. Paolo Guasoni & Eberhard Mayerhofer, 2020. "Technical Note—Options Portfolio Selection," Operations Research, INFORMS, vol. 68(3), pages 733-740, May.
    4. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
    5. Ian Martin, 2021. "On the Autocorrelation of the Stock Market [X-CAPM: An Extrapolative Capital Asset Pricing Model]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 39-52.
    6. Chabi-Yo, Fousseni & Loudis, Johnathan, 2020. "The conditional expected market return," Journal of Financial Economics, Elsevier, vol. 137(3), pages 752-786.

    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:bla:jfinan:v:74:y:2019:i:1:p:323-370. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/afaaaea.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.