This paper focuses on the estimation of mutual fund styles by return-based style analysis. Often the investment style is assumed to be constant through time. Alternatively, time variation is sometimes implicitly accounted for by using rolling regressions when estimating the style exposures. The former assumption is often contradicted empirically, and the latter is inefficient due to its ad hoc chosen window size. Here, the Kalman filter is used to model time-varying exposures of mutual funds explicitly. This leads to a testable model and more efficient use of the data, which reduces the influence of spurious correlation between mutual fund returns and style indices. Several stylized examples indicate that more reliable style estimates can be obtained by modelling the style exposure as a random walk, and estimating the coefficients with the Kalman filter. The differences with traditional techniques are substantial in these stylized examples. The results from the empirical analyses indicate that the structural model estimated by the Kalman filter improves style predictions and influences results on performance measurement.
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Volume (Year): 12 (2006) Issue (Month): 6-7 (October) Pages: 529-552 Download reference. The following formats are available: HTML
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ter Horst, Jenke R. & Nijman, Theo E. & de Roon, Frans A., 2004.
"Evaluating style analysis,"
Journal of Empirical Finance,
Elsevier, vol. 11(1), pages 29-53, January.
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Roon.F.A. de, & Nijman, T.E. & Horst, J.R. ter, 2000.
"Evaluating style analysis,"
Discussion Paper
64, Tilburg University, Center for Economic Research.
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Roon, F.A. de & Nijman, T.E. & Werker, B.J.M., 2000.
"Evaluating Style Analysis,"
Research Paper
ERS-2000-11-F&A Revision_, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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Stephen J. Brown & William N. Goetzmann, 2001.
"Hedge Funds With Style,"
NBER Working Papers
8173, National Bureau of Economic Research, Inc.
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