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Fat tails and spurious estimation of consumption‐based asset pricing models

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  • Alexis Akira Toda
  • Kieran James Walsh

Abstract

The standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous-agent consumption-based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from an incomplete-market dynamic general equilibrium model that is analytically solvable and exhibits power laws in consumption. Monte Carlo experiments suggest that the standard GMM estimation is inconsistent and susceptible to Type II errors (incorrect non-rejection of false models). Estimating an overidentified model by dividing agents into age cohorts appears to mitigate Type I and II errors.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Alexis Akira Toda & Kieran James Walsh, 2017. "Fat tails and spurious estimation of consumption‐based asset pricing models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1156-1177, September.
    • Handle: RePEc:wly:japmet:v:32:y:2017:i:6:p:1156-1177
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    Cited by:

    1. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    2. Beare, Brendan K & Toda, Alexis Akira, 2020. "On the emergence of a power law in the distribution of COVID-19 cases," University of California at San Diego, Economics Working Paper Series qt9k5027d0, Department of Economics, UC San Diego.
    3. Warusawitharana, Missaka, 2018. "Time-varying volatility and the power law distribution of stock returns," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 123-141.
    4. Gouin-Bonenfant, Emilien & Toda, Alexis Akira, 2018. "Pareto Extrapolation: Bridging Theoretical and Quantitative Models of Wealth Inequality," University of California at San Diego, Economics Working Paper Series qt90n2h2bb, Department of Economics, UC San Diego.
    5. Wilson, Matthew S., 2020. "Disaggregation and the equity premium puzzle," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 1-18.
    6. Alexis Akira Toda & Yulong Wang, 2021. "Efficient minimum distance estimation of Pareto exponent from top income shares," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 228-243, March.
    7. Alexis Akira Toda & Kieran James Walsh & Stijn Van Nieuwerburgh, 2020. "The Equity Premium and the One Percent," The Review of Financial Studies, Society for Financial Studies, vol. 33(8), pages 3583-3623.
    8. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020. "Exploiting ergodicity in forecasts of corporate profitability," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    9. Brendan K. Beare & Alexis Akira Toda, 2022. "Determination of Pareto Exponents in Economic Models Driven by Markov Multiplicative Processes," Econometrica, Econometric Society, vol. 90(4), pages 1811-1833, July.
    10. Toda, Alexis Akira, 2019. "Wealth distribution with random discount factors," Journal of Monetary Economics, Elsevier, vol. 104(C), pages 101-113.
    11. Abootaleb Shirvani & Stoyan V. Stoyanov & Frank J. Fabozzi & Svetlozar T. Rachev, 2021. "Equity premium puzzle or faulty economic modelling?," Review of Quantitative Finance and Accounting, Springer, vol. 56(4), pages 1329-1342, May.
    12. Émilien Gouin‐Bonenfant & Alexis Akira Toda, 2023. "Pareto extrapolation: An analytical framework for studying tail inequality," Quantitative Economics, Econometric Society, vol. 14(1), pages 201-233, January.
    13. de Castro, Luciano & Cundy, Lance D. & Galvao, Antonio F. & Westenberger, Rafael, 2023. "A dynamic quantile model for distinguishing intertemporal substitution from risk aversion," European Economic Review, Elsevier, vol. 159(C).
    14. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    15. Matthew S. Wilson, 2025. "Disaggregation Reverses the Risk-Free Rate Puzzle," Annals of Economics and Finance, Society for AEF, vol. 26(2), pages 643-665, November.

    More about this item

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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