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Robust Inference for Consumption‐Based Asset Pricing

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  • FRANK KLEIBERGEN
  • ZHAOGUO ZHAN

Abstract

The reliability of traditional asset pricing tests depends on: (i) the correlations between asset returns and factors; (ii) the time series sample size T compared to the number of assets N. For macro‐risk factors, like consumption growth, (i) and (ii) are often such that traditional tests cannot be trusted. We extend the Gibbons‐Ross‐Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, but show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.

Suggested Citation

  • Frank Kleibergen & Zhaoguo Zhan, 2020. "Robust Inference for Consumption‐Based Asset Pricing," Journal of Finance, American Finance Association, vol. 75(1), pages 507-550, February.
    • Handle: RePEc:bla:jfinan:v:75:y:2020:i:1:p:507-550
    DOI: 10.1111/jofi.12855
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    Cited by:

    1. Zhang, Xiang & Liu, Yangyi & Wu, Kun & Maillet, Bertrand, 2021. "Tradable or nontradable factors—what does the Hansen–Jagannathan distance tell us?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 853-879.
    2. Gospodinov, Nikolay & Robotti, Cesare, 2021. "Common pricing across asset classes: Empirical evidence revisited," Journal of Financial Economics, Elsevier, vol. 140(1), pages 292-324.
    3. Marie-Claude Beaulieu & Jean-Marie Dufour & Lynda Khalaf, 2020. "Arbitrage Pricing, Weak Beta, Strong Beta: Identification-Robust and Simultaneous Inference," Cahiers de recherche 15-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).
    5. 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.
    6. Guggenberger, Patrik & Kleibergen, Frank & Mavroeidis, Sophocles, 2023. "A test for Kronecker Product Structure covariance matrix," Journal of Econometrics, Elsevier, vol. 233(1), pages 88-112.
    7. Manresa, Elena & Peñaranda, Francisco & Sentana, Enrique, 2023. "Empirical evaluation of overspecified asset pricing models," Journal of Financial Economics, Elsevier, vol. 147(2), pages 338-351.
    8. Xu Cheng & Winston Wei Dou & Zhipeng Liao, 2022. "Macro‐Finance Decoupling: Robust Evaluations of Macro Asset Pricing Models," Econometrica, Econometric Society, vol. 90(2), pages 685-713, March.
    9. Laurinaityte, Nora & Meinerding, Christoph & Schlag, Christian & Thimme, Julian, 2020. "GMM weighting matrices incross-sectional asset pricing tests," Discussion Papers 62/2020, Deutsche Bundesbank.
    10. Gordon Schulze, 2021. "Carry Trade Returns and Segmented Risk Pricing," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 49(1), pages 23-40, March.
    11. Jinyong Kim & Kun Ho Kim & Jeong Hwan Lee, 2021. "Efficient Mimicking Portfolios in Asset Pricing Tests," Korean Economic Review, Korean Economic Association, vol. 37, pages 399-417.
    12. Beaulieu, Marie-Claude & Dufour, Jean-Marie & Khalaf, Lynda & Melin, Olena, 2023. "Identification-robust beta pricing, spanning, mimicking portfolios, and the benchmark neutrality of catastrophe bonds," Journal of Econometrics, Elsevier, vol. 236(1).
    13. Korsaye, Sofonias Alemu & Trojani, Fabio & Vedolin, Andrea, 2023. "The global factor structure of exchange rates," Journal of Financial Economics, Elsevier, vol. 148(1), pages 21-46.
    14. Frank Kleibergen & Zhaoguo Zhan, 2021. "Double robust inference for continuous updating GMM," Papers 2105.08345, arXiv.org.
    15. Frank Kleibergen & Zhaoguo Zhan, 2022. "Misspecification and Weak Identification in Asset Pricing," Papers 2206.13600, arXiv.org.
    16. Yukun Liu & Ben Matthies, 2022. "Long‐Run Risk: Is It There?," Journal of Finance, American Finance Association, vol. 77(3), pages 1587-1633, June.
    17. Lingwei Kong, 2023. "Weak (Proxy) Factors Robust Hansen-Jagannathan Distance For Linear Asset Pricing Models," Papers 2307.14499, arXiv.org.
    18. Bretscher, Lorenzo & Malkhozov, Aytek & Tamoni, Andrea, 2021. "Expectations and aggregate risk," Journal of Monetary Economics, Elsevier, vol. 123(C), pages 91-108.
    19. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
    20. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.
    21. Gruenthaler, Thomas & Lorenz, Friedrich & Meyerhof, Paul, 2022. "Option-based intermediary leverage," Journal of Banking & Finance, Elsevier, vol. 145(C).
    22. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.

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