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Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model

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  • Guo, Hui
  • Neely, Christopher J.

Abstract

Daily data and component GARCH (CGARCH) models strongly support a positive risk-return relation, in contrast to previous international results. Long-run volatility appears to be important in determining the conditional equity premium, but the evidence might be spurious.

Suggested Citation

  • Guo, Hui & Neely, Christopher J., 2008. "Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model," Economics Letters, Elsevier, vol. 99(2), pages 371-374, May.
  • Handle: RePEc:eee:ecolet:v:99:y:2008:i:2:p:371-374
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    Cited by:

    1. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    2. Kim Liow & Muhammad Ibrahim, 2010. "Volatility Decomposition and Correlation in International Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 40(2), pages 221-243, February.
    3. Li, Dandan & Ghoshray, Atanu & Morley, Bruce, 2012. "Measuring the risk premium in uncovered interest parity using the component GARCH-M model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 167-176.
    4. John Cotter & Enrique Salvador, 2014. "The non-linear trade-off between return and risk: a regime-switching multi-factor framework," Papers 1410.6005, arXiv.org.
    5. Angelos Kanas, 2013. "The risk-return relation and VIX: evidence from the S&P 500," Empirical Economics, Springer, vol. 44(3), pages 1291-1314, June.
    6. Łukasz Kwiatkowski, 2011. "Bayesian Analysis of a Regime Switching In-Mean Effect for the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 3(4), pages 187-219, December.
    7. Angelos Kanas, 2014. "Uncovering a positive risk-return relation: the role of implied volatility index," Review of Quantitative Finance and Accounting, Springer, vol. 42(1), pages 159-170, January.
    8. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    9. Pithak Srisuksai, 2012. "Idiosyncratic Volatility and Expected Stock Returns: Evidence from Thailand," Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 19(2), pages 66-89, December.
    10. Andrew Harvey & Rutger-Jan Lange, 2015. "Modeling the Interactions between Volatility and Returns," Cambridge Working Papers in Economics 1518, Faculty of Economics, University of Cambridge.
    11. Kanas, Angelos, 2012. "Modelling the risk–return relation for the S&P 100: The role of VIX," Economic Modelling, Elsevier, vol. 29(3), pages 795-809.
    12. Jiranyakul, Komain, 2011. "On the Risk-Return Tradeoff in the Stock Exchange of Thailand: New Evidence," MPRA Paper 45583, University Library of Munich, Germany.
    13. Mohanty, Roshni & P, Srinivasan, 2014. "The Time-Varying Risk and Return Trade Off in Indian Stock Markets," MPRA Paper 55660, University Library of Munich, Germany.

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