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The Cross Section of Expected Returns with MIDAS Betas

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  • González, Mariano
  • Nave, Juan
  • Rubio, Gonzalo

Abstract

This paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

Suggested Citation

  • González, Mariano & Nave, Juan & Rubio, Gonzalo, 2012. "The Cross Section of Expected Returns with MIDAS Betas," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(1), pages 115-135, February.
    • Handle: RePEc:cup:jfinqa:v:47:y:2012:i:01:p:115-135_00
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    Citations

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    Cited by:

    1. Cenesizoglu, Tolga & Reeves, Jonathan J., 2018. "CAPM, components of beta and the cross section of expected returns," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 223-246.
    2. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun & Wang, Weining, 2021. "Long- and short-run components of factor betas: Implications for stock pricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    3. repec:bof:bofitp:urn:nbn:fi:bof-201511231444 is not listed on IDEAS
    4. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    5. Jyri Kinnunen & Minna Martikainen, 2017. "Expected Returns and Idiosyncratic Risk: Industry-Level Evidence from Russia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2528-2544, November.
    6. John L. Glascock & Ran Lu-Andrews, 2018. "The Asymmetric Conditional Beta-Return Relations of REITs," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 231-245, August.
    7. Jan Schulz & Mishael Milaković, 2023. "How Wealthy are the Rich?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(1), pages 100-123, March.
    8. Belén Nieto & Alfonso Novales & Gonzalo Rubio, 2015. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-41, December.
    9. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
    10. repec:zbw:bofitp:urn:nbn:fi:bof-201511231444 is not listed on IDEAS
    11. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    12. repec:zbw:bofitp:2015_030 is not listed on IDEAS
    13. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    14. Pedro Antonio Martín-Cervantes & María del Carmen Valls Martínez, 2023. "Unraveling the relationship between betas and ESG scores through the Random Forests methodology," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-29, September.
    15. Stefano Grassi & Francesco Violante, 2021. "Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas," Working Papers 2021-05, Center for Research in Economics and Statistics.
    16. Huang, Lin & Wang, Zijun, 2014. "Is the investment factor a proxy for time-varying investment opportunities? The US and international evidence," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 219-232.
    17. Jyri Kinnunen & Minna Martikainen, 2017. "Expected Returns and Idiosyncratic Risk: Industry-Level Evidence from Russia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2528-2544, November.
    18. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
    19. Tolga Cenesizoglu & Denada Ibrushi, 2020. "Predicting Systematic Risk With Macroeconomic And Financial Variables," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(3), pages 649-673, August.
    20. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.

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