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Conditional risk-return relationship in a time-varying beta model

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  • Peng Huang
  • C. James Hueng

Abstract

We investigate the asymmetric risk-return relationship in a time-varying beta CAPM. A state space model is established and estimated by the Adaptive Least Squares with Kalman foundations proposed by McCulloch. Using S&P 500 daily data from 1987:11-2003:12, we find a positive risk-return relationship in the up market (positive market excess returns) and a negative relationship in the down market (negative market excess returns). This supports the argument of Pettengill et al., who use a constant beta model. However, our model outperforms theirs by eliminating the unexplained returns and improving the accuracy of the estimated risk price.

Suggested Citation

  • Peng Huang & C. James Hueng, 2008. "Conditional risk-return relationship in a time-varying beta model," Quantitative Finance, Taylor & Francis Journals, vol. 8(4), pages 381-390.
    • Handle: RePEc:taf:quantf:v:8:y:2008:i:4:p:381-390
    DOI: 10.1080/14697680701191361
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    References listed on IDEAS

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    1. Adrian, Tobias & Franzoni, Francesco, 2009. "Learning about beta: Time-varying factor loadings, expected returns, and the conditional CAPM," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 537-556, September.
    2. Don U. A. Galagedera & Robert Faff, 2005. "Modeling The Risk And Return Relation Conditional On Market Volatility And Market Conditions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 75-95.
    3. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
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    Cited by:

    1. Arouri, Mohamed El Hedi & Nguyen, Duc Khuong & Pukthuanthong, Kuntara, 2012. "An international CAPM for partially integrated markets: Theory and empirical evidence," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2473-2493.
    2. Yue-Jun Zhang & Yi-Ming Wei, 2011. "The dynamic influence of advanced stock market risk on international crude oil returns: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 967-978.
    3. Mohammad Enamul Hoque & Soo-Wah Low, 2020. "Industry Risk Factors and Stock Returns of Malaysian Oil and Gas Industry: A New Look with Mean Semi-Variance Asset Pricing Framework," Mathematics, MDPI, vol. 8(10), pages 1-28, October.
    4. James DiLellio, 2015. "A Kalman filter control technique in mean-variance portfolio management," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(2), pages 235-261, April.
    5. Zhang, Yue-Jun, 2011. "Interpreting the dynamic nexus between energy consumption and economic growth: Empirical evidence from Russia," Energy Policy, Elsevier, vol. 39(5), pages 2265-2272, May.
    6. Demirer, Rıza & Jategaonkar, Shrikant P., 2013. "The conditional relation between dispersion and return," Review of Financial Economics, Elsevier, vol. 22(3), pages 125-134.
    7. McNevin, Bruce D. & Nix, Joan, 2018. "The beta heuristic from a time/frequency perspective: A wavelet analysis of the market risk of sectors," Economic Modelling, Elsevier, vol. 68(C), pages 570-585.
    8. Guermat, Cherif & Freeman, Mark C., 2010. "A net beta test of asset pricing models," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 1-9, January.
    9. Claudio Morana, 2007. "Estimating, Filtering and Forecasting Realized Betas," ICER Working Papers - Applied Mathematics Series 6-2007, ICER - International Centre for Economic Research.

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