IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v32y2019i9p3617-3666..html

Does Smooth Ambiguity Matter for Asset Pricing?

Author

Listed:
  • A Ronald Gallant
  • Mohammad R Jahan-Parvar
  • Hening Liu

Abstract

We use the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. We rely on semi-nonparametric estimation of a flexible auxiliary model in our structural estimation. Based on the market and aggregate consumption data, our estimation provides statistical support for asset pricing models with smooth ambiguity. Statistical model comparison shows that models with ambiguity, learning, and time-varying volatility are preferred to the long-run risk model. We also analyze asset pricing implications of the estimated models. Received April 12, 2016; editorial decision September 11, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Suggested Citation

  • A Ronald Gallant & Mohammad R Jahan-Parvar & Hening Liu, 2019. "Does Smooth Ambiguity Matter for Asset Pricing?," The Review of Financial Studies, Society for Financial Studies, vol. 32(9), pages 3617-3666.
    • Handle: RePEc:oup:rfinst:v:32:y:2019:i:9:p:3617-3666.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhy118
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Han, Leyla Jianyu & Kasa, Kenneth & Luo, Yulei, 2024. "Ambiguity, information processing, and financial intermediation," Journal of Economic Theory, Elsevier, vol. 222(C).
    2. Aït-Sahalia, Yacine & Matthys, Felix & Osambela, Emilio & Sircar, Ronnie, 2025. "When uncertainty and volatility are disconnected: Implications for asset pricing and portfolio performance," Journal of Econometrics, Elsevier, vol. 248(C).
    3. Makarov, Dmitry, 2021. "Optimal portfolio under ambiguous ambiguity," Finance Research Letters, Elsevier, vol. 43(C).
    4. Sujoy Mukerji & Han N. Ozsoylev & Jean‐Marc Tallon, 2023. "Trading Ambiguity: A Tale Of Two Heterogeneities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1127-1164, August.
    5. Patrick Schmidt, 2019. "Eliciting ambiguity with mixing bets," Papers 1902.07447, arXiv.org, revised Aug 2024.
    6. Chiaki Hara & Toshiki Honda, 2022. "Implied Ambiguity: Mean-Variance Inefficiency and Pricing Errors," Management Science, INFORMS, vol. 68(6), pages 4246-4260, June.
    7. Yacine Aït-Sahalia & Felix Matthys & Emilio Osambela & Ronnie Sircar, 2021. "When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance," NBER Working Papers 29195, National Bureau of Economic Research, Inc.
    8. Liu, Liu, 2022. "Learning about the persistence of recessions under ambiguity aversion," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Chiaki Hara, 2023. "Arrow-Pratt-Type Measure of Ambiguity Aversion," KIER Working Papers 1097, Kyoto University, Institute of Economic Research.
    10. Cosmin L. Ilut & Martin Schneider, 2022. "Modeling Uncertainty as Ambiguity: a Review," NBER Working Papers 29915, National Bureau of Economic Research, Inc.
    11. Jin, Yurong & Yan, Jingzhou & Yan, Qianhui, 2024. "Unraveling ESG Ambiguity, Price Reaction, and Trading Volume," Finance Research Letters, Elsevier, vol. 61(C).
    12. David Alaminos & Ignacio Esteban & M. Belén Salas, 2023. "Neural networks for estimating Macro Asset Pricing model in football clubs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 57-75, April.
    13. Guillemin, François, 2020. "Governance by depositors, bank runs and ambiguity aversion," Research in International Business and Finance, Elsevier, vol. 54(C).
    14. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    15. Hening Liu & Yuzhao Zhang, 2022. "Financial Uncertainty with Ambiguity and Learning," Management Science, INFORMS, vol. 68(3), pages 2120-2140, March.
    16. Yang, Shuwen & Aretz, Kevin & Liu, Hening & Zhang, Yuzhao, 2022. "Consumption risks in option returns," Journal of Empirical Finance, Elsevier, vol. 69(C), pages 285-302.
    17. Fulop, Andras & Heng, Jeremy & Li, Junye & Liu, Hening, 2022. "Bayesian estimation of long-run risk models using sequential Monte Carlo," Journal of Econometrics, Elsevier, vol. 228(1), pages 62-84.
    18. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:rfinst:v:32:y:2019:i:9:p:3617-3666.. 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/sfsssea.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.