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The Estimation of Risk Premia with Omitted Variable Bias: Evidence from China

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  • Jie Mao

    (School of Economics, Shanghai University, No. 333 Nanchen Road Baoshan District, Shanghai 200444, China)

  • Tianliang Xia

    (School of Economics, Shanghai University, No. 333 Nanchen Road Baoshan District, Shanghai 200444, China)

Abstract

The Chinese stock market is replete with numerous omitted variables that can introduce biases in the standard estimation of risk premiums when traditional linear asset pricing models are applied. The three-pass method enables the estimation of risk premiums for observable factors even when not all relevant factors are explicitly specified or observed within the model. Accordingly, we have applied this method to construct portfolios with stocks from China’s A-share market as the test assets. Empirical research findings indicate that the three-pass method could be more effective than traditional linear asset pricing models in estimating risk premiums.

Suggested Citation

  • Jie Mao & Tianliang Xia, 2023. "The Estimation of Risk Premia with Omitted Variable Bias: Evidence from China," Risks, MDPI, vol. 11(12), pages 1-9, December.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:12:p:215-:d:1297867
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    References listed on IDEAS

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    1. Johnson, Travis L. & So, Eric C., 2012. "The option to stock volume ratio and future returns," Journal of Financial Economics, Elsevier, vol. 106(2), pages 262-286.
    2. Thomas J. Chemmanur & An Yan, 2019. "Advertising, Attention, and Stock Returns," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-51, September.
    3. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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