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Realized Beta: Persistence and Predictability

Author

Listed:
  • Torben G. Andersen

    (Department of Economics, Northwestern University)

  • Tim Bollerslev

    (Department of Economics, Duke University)

  • Francis X. Diebold

    (Department of Economics, University of Pennsylvania)

  • Jin Wu

    (Department of Economics, University of Pennsylvania)

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-Ã -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

  • Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2003. "Realized Beta: Persistence and Predictability," PIER Working Paper Archive 04-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Mar 2004.
    • Handle: RePEc:pen:papers:04-018
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    References listed on IDEAS

    as
    1. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
    2. Andreou, Elena & Ghysels, Eric, 2002. "Rolling-Sample Volatility Estimators: Some New Theoretical, Simulation, and Empirical Results," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 363-376, July.
    3. Asger Lunde & Peter Reinhard Hansen, 2004. "Realized Variance and IID Market Microstructure Noise," Econometric Society 2004 North American Summer Meetings 526, Econometric Society.
    4. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    5. Bollerslev, Tim & Zhang, Benjamin Y. B., 2003. "Measuring and modeling systematic risk in factor pricing models using high-frequency data," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 533-558, December.
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    Cited by:

    1. 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.
    2. Valadkhani, Abbas, 2023. "Asymmetric downside risk across different sectors of the US equity market," Global Finance Journal, Elsevier, vol. 57(C).
    3. 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).
    4. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    5. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. Levich, Richard M. & Potì, Valerio, 2015. "Predictability and ‘good deals’ in currency markets," International Journal of Forecasting, Elsevier, vol. 31(2), pages 454-472.
    13. Blasques, F. & Francq, Christian & Laurent, Sébastien, 2024. "Autoregressive conditional betas," Journal of Econometrics, Elsevier, vol. 238(2).
    14. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    15. 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.
    16. 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).

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    More about this item

    Keywords

    Quadratic variation and covariation; realized volatility; asset pricing; CAPM; equity betas; long memory; nonlinear fractional cointegration; continuous-time methods;
    All these keywords.

    JEL classification:

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

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