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Predicting Risk: Some New Generalizations

Author

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  • G. Andrew Karolyi

    (Academic Faculty of Finance, Ohio State University, Columbus, Ohio 43210)

Abstract

Existing adjustment techniques for forecasting systematic risk of individual firms have been based on relatively uniformative prior knowledge about the cross-sectional distribution of risk estimates. This study introduces prior information in the form of size and industry-based cross-sectional distributions of risk estimates. Such information is incorporated into forecasts using familiar and generalized adjustment techniques, the latter being based on recently developed multiple shrinkage methods. Improved forecast performance results.

Suggested Citation

  • G. Andrew Karolyi, 1992. "Predicting Risk: Some New Generalizations," Management Science, INFORMS, vol. 38(1), pages 57-74, January.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:1:p:57-74
    DOI: 10.1287/mnsc.38.1.57
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    Citations

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    Cited by:

    1. Tolga Cenesizoglu & Denada Ibrushi, 2020. "Predicting Systematic Risk With Macroeconomic And Financial Variables," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(3), pages 649-673, August.
    2. Cederburg, Scott & O’Doherty, Michael S., 2015. "Asset-pricing anomalies at the firm level," Journal of Econometrics, Elsevier, vol. 186(1), pages 113-128.
    3. I-Hsuan Ethan Chiang, 2016. "Skewness And Coskewness In Bond Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 39(2), pages 145-178, June.
    4. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2017. "How to Estimate Beta?," Hannover Economic Papers (HEP) dp-617, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    5. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "Estimating beta: Forecast adjustments and the impact of stock characteristics for a broad cross-section," Journal of Financial Markets, Elsevier, vol. 44(C), pages 91-118.
    6. Esteban González, María Victoria & Tusell Palmer, Fernando Jorge, 2009. "Predicting Betas: Two new methods," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    7. Antoinette Schoar & Kelvin Yeung & Luo Zuo, 2020. "The Effect of Managers on Systematic Risk," NBER Working Papers 27487, National Bureau of Economic Research, Inc.
    8. Wang, Jianqiu & Wu, Ke & Pan, Jiening, 2024. "On the conditional performance of the IVOL anomaly," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 337-350.
    9. Dekker, Lennart, 2024. "Essays on asset liquidity and investment funds," Other publications TiSEM 5fc9bf77-84e7-4a36-9e3a-1, Tilburg University, School of Economics and Management.
    10. Lee, Kuan-Hui, 2005. "The World Price of Liquidity Risk," Working Paper Series 2006-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    11. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).
    12. Tristan Jourde, 2022. "The Rising Interconnectedness of the Insurance Sector," Working papers 857, Banque de France.
    13. Villalba-Padilla, Fátima Irina & Flores-Ortega, Miguel, 2012. "Capacidad de predicción de los modelos GARCH simétricos aplicados a variables financieras de México 2001-2011," eseconomía, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(34), pages 81-124, segundo t.
    14. Yasser Alhenawi & M. Kabir Hassan, 2023. "How do investors price accrual risk during crises?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4684-4706, October.
    15. Martin R. Young & Peter J. Lenk, 1998. "Hierarchical Bayes Methods for Multifactor Model Estimation and Portfolio Selection," Management Science, INFORMS, vol. 44(11-Part-2), pages 111-124, November.
    16. Tristan Jourde, 2022. "The rising interconnectedness of the insurance sector," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(2), pages 397-425, June.
    17. Hollstein, Fabian, 2020. "Estimating beta: The international evidence," Journal of Banking & Finance, Elsevier, vol. 121(C).
    18. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2021. "Beta estimation in New Zealand," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    19. Mathijs Cosemans & Rik Frehen & Peter C. Schotman & Rob Bauer, 2016. "Estimating Security Betas Using Prior Information Based on Firm Fundamentals," The Review of Financial Studies, Society for Financial Studies, vol. 29(4), pages 1072-1112.
    20. Muradoglu, Gulnur & Zaman, Asad & Orhan, Mehmet, 2003. "Measuring the Systematic Risk of IPO’s Using Empirical Bayes Estimates in the Thinly Traded Istanbul Stock Exchange," MPRA Paper 13879, University Library of Munich, Germany.

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