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Asset Pricing without Garbage




This paper provides an explanation why garbage as a measure of consumption implies a several times lower coefficient of relative risk aversion in the consumption-based asset pricing model than consumption based on the official National Income and Product Accounts (NIPA): Unlike garbage, NIPA consumption is filtered to mitigate measurement error. I apply a structural model of the filtering process, which allows to revoke the filter inherent in NIPA consumption. Unfiltered NIPA consumption performs as well as garbage in explaining the equity premium and risk-free rate puzzle. Furthermore, I find that two other popular NIPA-based measures, three-year and fourth-quarter NIPA consumption, are related to unfiltered NIPA consumption. Both can be viewed as ad hoc unfilter rules.
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Suggested Citation

  • Tim A. Kroencke, 2017. "Asset Pricing without Garbage," Journal of Finance, American Finance Association, vol. 72(1), pages 47-98, February.
  • Handle: RePEc:bla:jfinan:v:72:y:2017:i:1:p:47-98

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    References listed on IDEAS

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    2. Alexi Savov, 2011. "Asset Pricing with Garbage," Journal of Finance, American Finance Association, vol. 66(1), pages 177-201, February.
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    2. Schlag, Christian & Semenischev, Michael & Thimme, Julian, 2020. "Predictability and the cross-section of expected returns: A challenge for asset pricing models," SAFE Working Paper Series 289, Leibniz Institute for Financial Research SAFE.
    3. Paulo Rogério Faustino Matos, 2019. "The role of household debt and delinquency decisions in consumption-based asset pricing," Annals of Finance, Springer, vol. 15(2), pages 179-203, June.
    4. Chen, Zhanhui & Yang, Bowen, 2019. "In search of preference shock risks: Evidence from longevity risks and momentum profits," Journal of Financial Economics, Elsevier, vol. 133(1), pages 225-249.
    5. Ashley Lim & Yihui Lan & Sirimon Treepongkaruna, 2020. "Asset pricing and energy consumption risk," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3813-3850, December.
    6. Nam, Eun-Young & Lee, Kiryoung & Jeon, Yoontae, 2021. "Macroeconomic uncertainty shocks and households’ consumption choice," Journal of Macroeconomics, Elsevier, vol. 68(C).
    7. Li, Huan, 2020. "Asset pricing with long-run durable expenditure risk," Finance Research Letters, Elsevier, vol. 32(C).
    8. Mykola Babiak & Roman Kozhan, 2021. "Growth Uncertainty, Rational Learning, and Option Prices," CERGE-EI Working Papers wp682, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Zhi Da & Wei Yang & Hayong Yun, 2016. "Household Production and Asset Prices," Management Science, INFORMS, vol. 62(2), pages 387-409, February.
    10. Gregory Connor & Robert A. Korajczyk, 2019. "Semi-strong factors in asset returns," Economics Department Working Paper Series n294-19.pdf, Department of Economics, National University of Ireland - Maynooth.
    11. Laurinaityte, Nora & Meinerding, Christoph & Schlag, Christian & Thimme, Julian, 2020. "GMM weighting matrices incross-sectional asset pricing tests," Discussion Papers 62/2020, Deutsche Bundesbank.
    12. de Oliveira Souza, Thiago, 2018. "Red tape asset pricing," Discussion Papers of Business and Economics 8/2018, University of Southern Denmark, Department of Business and Economics.
    13. Ferreira, Alex & Matos, Paulo, 2020. "Precautionary risks for an open economy," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 154-167.

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates


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