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Measuring “Dark Matter” in Asset Pricing Models

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Listed:
  • Hui Chen
  • Winston Wei Dou
  • Leonid Kogan

Abstract

We formalize the concept of “dark matter” in asset pricing models by quantifying the additional informativeness of cross-equation restrictions about fundamental dynamics. The dark matter measure captures the degree of fragility for models that are potentially misspecified and unstable: a large dark matter measure signifies that the model lacks internal refutability (weak power of optimal specification tests) and external validity (high overfitting tendency and poor out-of-sample fit). The measure can be computed at low cost even for complex dynamic structural models. To illustrate its applications, we provide quantitative examples applying the measure to (time-varying) rare-disaster risk and long-run risk models.

Suggested Citation

  • Hui Chen & Winston Wei Dou & Leonid Kogan, 2019. "Measuring “Dark Matter” in Asset Pricing Models," NBER Working Papers 26418, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26418
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    Cited by:

    1. Robert Barro & Tao Jin, 2021. "Rare Events and Long-Run Risks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 39, pages 1-25, January.
    2. Matthew Baron & Wei Xiong, 2017. "Credit Expansion and Neglected Crash Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 713-764.
    3. Christian Schlag & Michael Semenischev & Julian Thimme, 2021. "Predictability and the Cross-Section of Expected Returns: A Challenge for Asset Pricing Models," Management Science, INFORMS, vol. 67(12), pages 7932-7950, December.
    4. Augustin, P. & Chernov, M. & Schmid, L. & Song, D., 2021. "Benchmark interest rates when the government is risky," Journal of Financial Economics, Elsevier, vol. 140(1), pages 74-100.
    5. Jerry Tsai & Jessica A. Wachter, 2014. "Rare Booms and Disasters in a Multi-sector Endowment Economy," NBER Working Papers 20062, National Bureau of Economic Research, Inc.
    6. Hansen, Lars Peter & Sargent, Thomas J., 2021. "Macroeconomic uncertainty prices when beliefs are tenuous," Journal of Econometrics, Elsevier, vol. 223(1), pages 222-250.
    7. John H. Cochrane, 2017. "Macro-Finance," Review of Finance, European Finance Association, vol. 21(3), pages 945-985.
    8. Hassan Afrouzi & Laura Veldkamp, 2019. "Biased Inflation Forecasts," 2019 Meeting Papers 894, Society for Economic Dynamics.
    9. Lars P. Hansen & Thomas J. Sargent, 2016. "Sets of Models and Prices of Uncertainty," NBER Working Papers 22000, National Bureau of Economic Research, Inc.
    10. Robert Barro & Tao Jin, 2021. "Rare Events and Long-Run Risks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 39, pages 1-25, January.

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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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