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Do Jumps Contribute to the Dynamics of the Equity Premium?

Author

Listed:
  • John M. Maheu

    () (Department of Economics, University of Toronto, Canada; RCEA, Italy)

  • Thomas H. McCurdy

    () (Rotman School of Managment, University of Toronto, Canada; CIRANO, Canada)

  • Xiaofei Zhao

    () (Rotman School of Management, University of Toronto, Canada)

Abstract

This paper investigates whether risks associated with time-varying arrival of jumps and their effect on the dynamics of higher moments of returns are priced in the conditional mean of daily market excess returns. We find that jumps and jump dynamics are significantly related to the market equity premium. The results from our time-series approach reinforce the importance of the skewness premium found in cross-sectional studies using lower-frequency data; and offer a potential resolution to sometimes conflicting results on the intertemporal risk-return relationship. We use a general utility specification, consistent with our pricing kernel, to evaluate the relative value of alternative risk premium models in an out-of-sample portfolio performance application.

Suggested Citation

  • John M. Maheu & Thomas H. McCurdy & Xiaofei Zhao, 2012. "Do Jumps Contribute to the Dynamics of the Equity Premium?," Working Paper series 47_12, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:47_12
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    References listed on IDEAS

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    Cited by:

    1. Kuttu, Saint, 2017. "Time-varying conditional discrete jumps in emerging African equity markets," Global Finance Journal, Elsevier, vol. 32(C), pages 35-54.
    2. Demirer, Riza & Gupta, Rangan & Suleman, Tahir & Wohar, Mark E., 2018. "Time-varying rare disaster risks, oil returns and volatility," Energy Economics, Elsevier, vol. 75(C), pages 239-248.
    3. Piccotti, Louis R., 2018. "Jumps, cojumps, and efficiency in the spot foreign exchange market," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 49-67.
    4. Li, Chenxing & Maheu, John M, 2020. "A Multivariate GARCH-Jump Mixture Model," MPRA Paper 104770, University Library of Munich, Germany.
    5. Li, Gang & Zhang, Chu, 2016. "On the relationship between conditional jump intensity and diffusive volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 196-213.
    6. Mark J. Jensen & John M. Maheu, 2018. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(3), pages 1-1, September.
    7. Erdemlioglu, Deniz & Laurent, Sébastien & Neely, Christopher J., 2015. "Which continuous-time model is most appropriate for exchange rates?," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 256-268.
    8. Cerrato, Mario & Crosby, John & Kim, Minjoo & Zhao, Yang, 2017. "Relation between higher order comoments and dependence structure of equity portfolio," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 101-120.
    9. Peter Christoffersen & Bruno Feunou & Yoontae Jeon & Chayawat Ornthanalai, 2016. "Time-Varying Crash Risk: The Role of Stock Market Liquidity," Staff Working Papers 16-35, Bank of Canada.
    10. Xiao, Xiao & Zhou, Chen, 2018. "The decomposition of jump risks in individual stock returns," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 207-228.
    11. Zhou, Chunyang & Wu, Chongfeng & Wang, Yudong, 2019. "Dynamic portfolio allocation with time-varying jump risk," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 113-124.
    12. Marco Bee & Debbie J. Dupuis & Luca Trapin, 2016. "US stock returns: are there seasons of excesses?," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1453-1464, September.
    13. Nolte, Ingmar & Xu, Qi, 2015. "The economic value of volatility timing with realized jumps," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 45-59.
    14. Chunyang Zhou & Chongfeng Wu & Weidong Xu, 2020. "Incorporating time‐varying jump intensities in the mean‐variance portfolio decisions," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 460-478, March.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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