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Does Smooth Ambiguity Matter for Asset Pricing?

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  • 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.
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    1. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean‐Marc Tallon, 2018. "Ambiguity and the historical equity premium," Quantitative Economics, Econometric Society, vol. 9(2), pages 945-993, July.
    2. Guerrieri, Luca & Iacoviello, Matteo, 2015. "OccBin: A toolkit for solving dynamic models with occasionally binding constraints easily," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 22-38.
    3. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
    4. Larry G. Epstein & Stanley E. Zin, 2013. "Substitution, risk aversion and the temporal behavior of consumption and asset returns: A theoretical framework," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 12, pages 207-239, World Scientific Publishing Co. Pte. Ltd..
    5. Nengjiu Ju & Jianjun Miao, 2012. "Ambiguity, Learning, and Asset Returns," Econometrica, Econometric Society, vol. 80(2), pages 559-591, March.
    6. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean‐Marc Tallon, 2018. "Ambiguity and the historical equity premium," Quantitative Economics, Econometric Society, vol. 9(2), pages 945-993, July.
    7. Walter Pohl & Karl Schmedders & Ole Wilms, 2018. "Higher Order Effects in Asset Pricing Models with Long‐Run Risks," Journal of Finance, American Finance Association, vol. 73(3), pages 1061-1111, June.
    8. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    9. Dario Caldara & Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Wen Yao, 2012. "Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(2), pages 188-206, April.
    10. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
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    Cited by:

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    2. 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.
    3. Makarov, Dmitry, 2021. "Optimal portfolio under ambiguous ambiguity," Finance Research Letters, Elsevier, vol. 43(C).
    4. Patrick Schmidt, 2019. "Eliciting ambiguity with mixing bets," Papers 1902.07447, arXiv.org, revised Aug 2024.
    5. Chiaki Hara & Toshiki Honda, 2022. "Implied Ambiguity: Mean-Variance Inefficiency and Pricing Errors," Management Science, INFORMS, vol. 68(6), pages 4246-4260, June.
    6. Liu, Liu, 2022. "Learning about the persistence of recessions under ambiguity aversion," Finance Research Letters, Elsevier, vol. 47(PA).
    7. Chiaki Hara, 2023. "Arrow-Pratt-Type Measure of Ambiguity Aversion," KIER Working Papers 1097, Kyoto University, Institute of Economic Research.
    8. Cosmin L. Ilut & Martin Schneider, 2022. "Modeling Uncertainty as Ambiguity: a Review," NBER Working Papers 29915, National Bureau of Economic Research, Inc.
    9. Jin, Yurong & Yan, Jingzhou & Yan, Qianhui, 2024. "Unraveling ESG Ambiguity, Price Reaction, and Trading Volume," Finance Research Letters, Elsevier, vol. 61(C).
    10. 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.
    11. 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.
    12. Guillemin, François, 2020. "Governance by depositors, bank runs and ambiguity aversion," Research in International Business and Finance, Elsevier, vol. 54(C).
    13. Hening Liu & Yuzhao Zhang, 2022. "Financial Uncertainty with Ambiguity and Learning," Management Science, INFORMS, vol. 68(3), pages 2120-2140, March.
    14. 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.
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    16. Andras Fulop & Jeremy Heng & Junye Li, 2022. "Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models," Papers 2201.01094, arXiv.org.

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    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

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