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Asset pricing models in the presence of higher moments: Theory and evidence from the U.S. and China stock market

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  • Hu, Debao
  • Li, Xin
  • Xiang, George
  • Zhou, Qiyao

Abstract

Harvey and Siddique (2000) show that a security's coskewness, measured by the comovement of its stock return and the variance of market return, significantly explains its stock performance. We extend this idea in two significant ways. Conceptually, we show that the comovements of individual security performance and higher moments of market performance are critical components of asset return determinants. Empirically, we examine and compare the performance of high-moment capital asset pricing models (CAPM) in the U.S. and Chinese stock markets. The empirical results show that the coskewness and cokurtosis of securities have a significant impact on their performance. We observed that models incorporating higher moments provide greater explanatory power than the traditional CAPM model, particularly in the Chinese market. This is due to the high sensitivity of stocks in this market to tail risks, which can be attributed to the market's immaturity and the higher proportion of individual investors.

Suggested Citation

  • Hu, Debao & Li, Xin & Xiang, George & Zhou, Qiyao, 2023. "Asset pricing models in the presence of higher moments: Theory and evidence from the U.S. and China stock market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
  • Handle: RePEc:eee:pacfin:v:79:y:2023:i:c:s0927538x23001191
    DOI: 10.1016/j.pacfin.2023.102053
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    as
    1. Liu, Jianan & Stambaugh, Robert F. & Yuan, Yu, 2019. "Size and value in China," Journal of Financial Economics, Elsevier, vol. 134(1), pages 48-69.
    2. Kon, Stanley J, 1984. "Models of Stock Returns-A Comparison," Journal of Finance, American Finance Association, vol. 39(1), pages 147-165, March.
    3. Kostakis, Alexandros & Muhammad, Kashif & Siganos, Antonios, 2012. "Higher co-moments and asset pricing on London Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 913-922.
    4. Markus K. Brunnermeier & Jonathan A. Parker & Christian Gollier, 2007. "Optimal Beliefs, Asset Prices, and the Preference for Skewed Returns," American Economic Review, American Economic Association, vol. 97(2), pages 159-165, May.
    5. Jianhua Gang & Zongxin Qian & Fan Chen, 2019. "The Aumann-Serrano risk factor and asset pricing: evidence from the Chinese A-share market," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1599-1608, October.
    6. Thierry Foucault & David Sraer & David J. Thesmar, 2011. "Individual Investors and Volatility," Journal of Finance, American Finance Association, vol. 66(4), pages 1369-1406, August.
    7. Hwang, Soosung & Satchell, Stephen E, 1999. "Modelling Emerging Market Risk Premia Using Higher Moments," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 4(4), pages 271-296, October.
    8. Bollerslev, Tim & Todorov, Viktor & Xu, Lai, 2015. "Tail risk premia and return predictability," Journal of Financial Economics, Elsevier, vol. 118(1), pages 113-134.
    9. Zhiguo He & Arvind Krishnamurthy, 2013. "Intermediary Asset Pricing," American Economic Review, American Economic Association, vol. 103(2), pages 732-770, April.
    10. Robert J. Aumann & Roberto Serrano, 2008. "An Economic Index of Riskiness," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 810-836, October.
    11. Rubinstein, Mark E., 1973. "The Fundamental Theorem of Parameter-Preference Security Valuation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 8(1), pages 61-69, January.
    12. Oussama Tilfani & Paulo Ferreira & My Youssef El Boukfaoui, 2020. "Multiscale optimal portfolios using CAPM fractal regression: estimation for emerging stock markets," Post-Communist Economies, Taylor & Francis Journals, vol. 32(1), pages 77-112, January.
    13. Fang, Hsing & Lai, Tsong-Yue, 1997. "Co-Kurtosis and Capital Asset Pricing," The Financial Review, Eastern Finance Association, vol. 32(2), pages 293-307, May.
    14. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    15. Jennifer Conrad & Robert F. Dittmar & Eric Ghysels, 2013. "Ex Ante Skewness and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 68(1), pages 85-124, February.
    16. Homm, Ulrich & Pigorsch, Christian, 2012. "Beyond the Sharpe ratio: An application of the Aumann–Serrano index to performance measurement," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2274-2284.
    17. Brandon Flores & Blessing Ofori-Atta & Andrey Sarantsev, 2021. "A stock market model based on CAPM and market size," Annals of Finance, Springer, vol. 17(3), pages 405-424, September.
    18. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    19. Bai, Jennie & Bali, Turan G. & Wen, Quan, 2019. "Common risk factors in the cross-section of corporate bond returns," Journal of Financial Economics, Elsevier, vol. 131(3), pages 619-642.
    20. Lee, Kuan-Hui & Yang, Cheol-Won, 2022. "The world price of tail risk," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    21. Chen, Xuanjuan & Kim, Kenneth A. & Yao, Tong & Yu, Tong, 2010. "On the predictability of Chinese stock returns," Pacific-Basin Finance Journal, Elsevier, vol. 18(4), pages 403-425, September.
    22. Andrey Sarantsev & Blessing Ofori-Atta & Brandon Flores, 2019. "A Stock Market Model Based on CAPM and Market Size," Papers 1907.08911, arXiv.org, revised Apr 2021.
    23. Yaron Levi & Ivo Welch & Andrew Karolyi, 2020. "Symmetric and Asymmetric Market Betas and Downside Risk," The Review of Financial Studies, Society for Financial Studies, vol. 33(6), pages 2772-2795.
    24. Johan Knif & Dimitrios Koutmos & Gregory Koutmos, 2020. "Higher Co-Moment CAPM and Hedge Fund Returns," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(1), pages 99-113, March.
    25. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
    26. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    27. Yang, Xiaolan & Zhu, Yu & Cheng, Teng Yuan, 2020. "How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
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    Cited by:

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    3. An Pham Ngoc Nguyen & Thomas Conlon & Martin Crane & Marija Bezbradica, 2024. "Herding Unmasked: Insights into Cryptocurrencies, Stocks and US ETFs," Papers 2407.08069, arXiv.org.

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    More about this item

    Keywords

    Asset pricing; Higher moments; Tail risks;
    All these keywords.

    JEL classification:

    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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