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Realized beta: Persistence and predictability

Author

Listed:
  • Andersen, Torben G.
  • Bollerslev, Tim
  • Diebold, Francis X.
  • Wu, Jin

Abstract

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally-integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.

Suggested Citation

  • Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Wu, Jin, 2004. "Realized beta: Persistence and predictability," CFS Working Paper Series 2004/16, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:200416
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    Cited by:

    1. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    2. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
    3. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.
    4. Alfred Mbairadjim Moussa & Jules Sadefo Kamdem & Arnold F. Shapiro & Michel Terraza, 2012. "Capital asset pricing model with fuzzy returns and hypothesis testing," Working Papers 12-33, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    5. Leong, Minhao & Alexeev, Vitali & Kwok, Simon, 2025. "Managing cryptocurrency risk exposures in equity portfolios: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 99(C).
    6. Messis, Petros & Alexandridis, Antonios K. & Zapranis, Achilleas, 2025. "A qualitative parameter for beta changes," International Review of Economics & Finance, Elsevier, vol. 103(C).
    7. Stoja, Evarist & Polanski, Arnold & Nguyen, Linh H. & Pereverzin, Aleksandr, 2023. "Does systematic tail risk matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    8. Blasques, F. & Francq, Christian & Laurent, Sébastien, 2024. "Autoregressive conditional betas," Journal of Econometrics, Elsevier, vol. 238(2).
    9. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    10. Haselmann, Rainer & Herwartz, Helmut, 2008. "Portfolio performance and the Euro: Prospects for new potential EMU members," Journal of International Money and Finance, Elsevier, vol. 27(2), pages 314-330, March.
    11. Jose Fernandes & Augusto Hasman & Juan Ignacio Pena, 2007. "Risk premium: insights over the threshold," Applied Financial Economics, Taylor & Francis Journals, vol. 18(1), pages 41-59.
    12. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).
    13. Mbairadjim Moussa, A. & Sadefo Kamdem, J. & Shapiro, A.F. & Terraza, M., 2014. "CAPM with fuzzy returns and hypothesis testing," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 40-57.
    14. Jan Sila & Michael Mark & Ladislav Kristoufek & Thomas A. Weber, 2025. "Crypto market betas: the limits of predictability and hedging," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-28, December.
    15. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    16. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    17. Berger, David & Chaboud, Alain & Hjalmarsson, Erik, 2009. "What drives volatility persistence in the foreign exchange market?," Journal of Financial Economics, Elsevier, vol. 94(2), pages 192-213, November.
    18. Papavassiliou, Vassilios G., 2013. "A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 184-197.
    19. Quaye, Enoch & Tunaru, Diana & Tunaru, Radu, 2024. "Green-adjusted share prices: A comparison between standard investors and investors with green preferences," Journal of Financial Stability, Elsevier, vol. 74(C).
    20. Levich, Richard M. & Potì, Valerio, 2015. "Predictability and ‘good deals’ in currency markets," International Journal of Forecasting, Elsevier, vol. 31(2), pages 454-472.
    21. Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.
    22. Luo, Jiawen & Chen, Zhenbiao & Cheng, Mingmian, 2025. "Forecasting realized betas using predictors indicating structural breaks and asymmetric risk effects," Journal of Empirical Finance, Elsevier, vol. 80(C).
    23. Kalnina, Ilze & Tewou, Kokouvi, 2025. "Cross-sectional dependence in idiosyncratic volatility," Journal of Econometrics, Elsevier, vol. 249(PB).

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G1 - Financial Economics - - General Financial Markets

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