IDEAS home Printed from https://ideas.repec.org/p/red/sed016/306.html
   My bibliography  Save this paper

Higher-Order Effects in Asset-Pricing Models with Long-Run Risks

Author

Listed:
  • Ole Wilms

    (University of Zurich)

  • Karl Schmedders

    (University of Zurich)

  • Walt Pohl

    (University of Zurich)

Abstract

This paper analyzes both the existence of solutions to long-run risk asset pricing models as well as the practicality of approximating these solutions by the Campbell-Shiller log-linearization. We prove a simple relative existence result that is sufficient to show that the original Bansal-Yaron model has a solution. Log-linearization fares less well: we find that for very persistent processes the approximation errors in model moments can be as large as 50%, and can get such basic facts wrong as the direction of the yield curve. The increasing complexity of state-of-the-art asset-pricing models can lead to complex nonlinear solutions with considerable curvature, which in turn can have sizable economic implications. Therefore, these models require numerical solution methods, such as the projection methods employed in this paper, that can adequately describe the higher-order equilibrium features.

Suggested Citation

  • Ole Wilms & Karl Schmedders & Walt Pohl, 2016. "Higher-Order Effects in Asset-Pricing Models with Long-Run Risks," 2016 Meeting Papers 306, Society for Economic Dynamics.
  • Handle: RePEc:red:sed016:306
    as

    Download full text from publisher

    File URL: https://economicdynamics.org/meetpapers/2016/paper_306.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Burnside, Craig, 1998. "Solving asset pricing models with Gaussian shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 22(3), pages 329-340, March.
    2. Collard, Fabrice & F Ve, Patrick & Ghattassi, Imen, 2006. "A Note On The Exact Solution Of Asset Pricing Models With Habit Persistence," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 273-283, April.
    3. Attanasio, Orazio P & Weber, Guglielmo, 1995. "Is Consumption Growth Consistent with Intertemporal Optimization? Evidence from the Consumer Expenditure Survey," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1121-1157, December.
    4. Dew-Becker, Ian & Giglio, Stefano & Le, Anh & Rodriguez, Marius, 2017. "The price of variance risk," Journal of Financial Economics, Elsevier, vol. 123(2), pages 225-250.
    5. Bollerslev, Tim & Xu, Lai & Zhou, Hao, 2015. "Stock return and cash flow predictability: The role of volatility risk," Journal of Econometrics, Elsevier, vol. 187(2), pages 458-471.
    6. Flodén, Martin, 2008. "A note on the accuracy of Markov-chain approximations to highly persistent AR(1) processes," Economics Letters, Elsevier, vol. 99(3), pages 516-520, June.
    7. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
    8. Bjørn Eraker & Ivan Shaliastovich, 2008. "An Equilibrium Guide To Designing Affine Pricing Models," Mathematical Finance, Wiley Blackwell, vol. 18(4), pages 519-543, October.
    9. Alexander Meyer-Gohde, 2014. "Risky Linear Approximations," SFB 649 Discussion Papers SFB649DP2014-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Frank Schorfheide & Dongho Song & Amir Yaron, 2013. "Identifying long-run risks: a bayesian mixed-frequency approach," Working Papers 13-39, Federal Reserve Bank of Philadelphia.
    11. Epstein, Larry G & Zin, Stanley E, 1989. "Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: A Theoretical Framework," Econometrica, Econometric Society, vol. 57(4), pages 937-969, July.
    12. Bidarkota, Prasad V. & McCulloch, J. Huston, 2003. "Consumption asset pricing with stable shocks--exploring a solution and its implications for mean equity returns," Journal of Economic Dynamics and Control, Elsevier, vol. 27(3), pages 399-421, January.
    13. de Groot, Oliver, 2013. "Computing the risky steady state of DSGE models," Economics Letters, Elsevier, vol. 120(3), pages 566-569.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. A. Ronald Gallant & Mohammad Jahan-Parvar & Hening Liu, 2018. "Does Smooth Ambiguity Matter for Asset Pricing?," International Finance Discussion Papers 1221, Board of Governors of the Federal Reserve System (U.S.).
    2. Alexis Akira Toda, 2018. "Data-based Automatic Discretization of Nonparametric Distributions," Papers 1805.00896, arXiv.org, revised May 2019.
    3. Ivan Sutoris, 2018. "Asset Prices in a Production Economy with Long Run and Idiosyncratic Risk," CERGE-EI Working Papers wp620, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    4. Oliver de Groot & Alexander W. Richter & Nathaniel A. Throckmorton, 2018. "Valuation Risk Revalued," CDMA Working Paper Series 201803, Centre for Dynamic Macroeconomic Analysis.
    5. Andreas Tryphonides, 2018. "Equilibrium Restrictions and Approximate Models: Pricing Macroeconomic Risk," Papers 1805.10869, arXiv.org.
    6. Myroslav Pidkuyko & Raffaele Rossi & Klaus Reiner Schenk-Hoppé, 2019. "The Resolution of Long-Run Risk," The School of Economics Discussion Paper Series 1908, Economics, The University of Manchester.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:red:sed016:306. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christian Zimmermann). General contact details of provider: http://edirc.repec.org/data/sedddea.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.