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Estimating Beta

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  • Hollstein, Fabian
  • Prokopczuk, Marcel

Abstract

We conduct a comprehensive comparison of market beta estimation techniques. We study the performance of several historical, time-series model, and option-implied estimators for estimating realized market beta. Thereby, we find the hybrid methodology of Buss and Vilkov to consistently outperform all other approaches. In addition, all other approaches, including fully implied and dynamic conditional beta, based on generalized autoregressive conditional heteroskedasticity (GARCH) models, are dominated by a simple beta estimate based on historical (co-)variances and an approach based on the Kalman filter. Our conclusions remain unchanged after performing several robustness checks.

Suggested Citation

  • Hollstein, Fabian & Prokopczuk, Marcel, 2016. "Estimating Beta," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(4), pages 1437-1466, August.
    • Handle: RePEc:cup:jfinqa:v:51:y:2016:i:04:p:1437-1466_00
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    References listed on IDEAS

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    1. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    2. Adrian Buss & Grigory Vilkov, 2012. "Measuring Equity Risk with Option-implied Correlations," The Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3113-3140.
    3. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
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    1. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "How to Estimate Beta?," Hannover Economic Papers (HEP) dp-617, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    2. Korn, Olaf & Kuntz, Laura-Chloé, 2015. "Low-beta investment strategies," CFR Working Papers 15-17, University of Cologne, Centre for Financial Research (CFR).
    3. Thomas Conlon & John Cotter & Ramazan Gençay, 2015. "Long-run international diversification," Working Papers 201502, Geary Institute, University College Dublin.
    4. Fabian Hollstein & Marcel Prokopczuk & Chardin Wese Simen, 2019. "The term structure of systematic and idiosyncratic risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 435-460, April.

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