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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:

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    2. Kuttu, Saint, 2017. "Time-varying conditional discrete jumps in emerging African equity markets," Global Finance Journal, Elsevier, vol. 32(C), pages 35-54.
    3. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "The role of time‐varying rare disaster risks in predicting bond returns and volatility," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 327-340, July.
    4. 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.
    5. Jozef Barunik & Josef Kurka, 2021. "Risks of heterogeneously persistent higher moments," Papers 2104.04264, arXiv.org, revised Mar 2024.
    6. Li, Chenxing & Maheu, John M, 2020. "A Multivariate GARCH-Jump Mixture Model," MPRA Paper 104770, University Library of Munich, Germany.
    7. Mark J. Jensen & John M. Maheu, 2018. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-29, September.
    8. 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.
    9. 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.
    10. Aysan, Ahmet Faruk & Caporin, Massimiliano & Cepni, Oguzhan, 2024. "Not all words are equal: Sentiment and jumps in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    11. 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.
    12. 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.
    13. 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.
    14. Dierkes, Maik & Hollstein, Fabian & Prokopczuk, Marcel & Würsig, Christoph Matthias, 2024. "Measuring tail risk," Journal of Econometrics, Elsevier, vol. 241(2).
    15. 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.
    16. Qian, Ya & Tu, Jun & Härdle, Wolfgang Karl, 2019. "Information Arrival, News Sentiment, Volatilities and Jumps of Intraday Returns," IRTG 1792 Discussion Papers 2019-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. 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.
    18. Meng, Yongqiang & Li, Xiao & Xiong, Xiong, 2024. "Information shocks and short-term market overreaction: The role of investor attention," International Review of Financial Analysis, Elsevier, vol. 93(C).
    19. Zhang, Yuan-Yuan & Zhang, Yue-Jun, 2022. "The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    20. 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.
    21. 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.
    22. Thomas Conlon & John Cotter & Chenglu Jin, 2016. "The Intervaling Effect on Higher-Order Co-Moments," Working Papers 201602, Geary Institute, University College Dublin.
    23. 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.
    24. Jeon, Yoontae & McCurdy, Thomas H. & Zhao, Xiaofei, 2022. "News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies," Journal of Financial Economics, Elsevier, vol. 145(2), pages 1-17.

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    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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