Hierarchical Bayes Methods for Multifactor Model Estimation and Portfolio Selection

Martin R. Young and Peter J. Lenk
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Martin R. Young: University of Michigan School of Business, Department of Statistics and Management Science, Ann Arbor, Michigan 48109-1234
Peter J. Lenk: University of Michigan School of Business, Department of Statistics and Management Science, Ann Arbor, Michigan 48109-1234

Management Science, 1998, vol. 44, issue 11-Part-2, S111-S124

Abstract: The factor model is an important construct for both portfolio managers and researchers in modern finance. For practitioners, factor model coefficients are used to guide the construction of optimal portfolios. For academicians, factor model parameters play a fundamental role in explaining equilibrium asset prices and other market phenomena. This paper presents a hierarchical modeling procedure that can substantially improve the accuracy of factor model parameter estimates through incorporation of cross-sectional information. It is shown that this improvement in parameter estimation accuracy translates into substantial improvement in portfolio performance. Expressions are derived that characterize the sensitivity of portfolio performance to parameter estimation error. Evidence with NYSE data suggests that the hierarchical estimation technique leads to superior out-of-sample portfolio performance when compared to alternative estimation approaches.

Keywords: Beta; Estimation Risk; Markov Chain Monte Carlo; Sensitivity; Shrinkage (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:44:y:1998:i:11-part-2:p:s111-s124

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