errors-in-variables; measurement errors; hedge fund performance
Abstract :
[en] In linear models for hedge fund returns, errors-in-variables may significantly alter the measurement of factor loadings and the estimation of abnormal performance. The higher moment estimator (HME) introduced by Dagenais and Dagenais (1997) effectively deals with these issues. Results on individual funds show that the HME specification does not uncover systematic performance biases, but can modify estimated alphas in most cases and identifies relative persistence for directional funds in bearish market conditions. Overall, the risk premia calculated with HME remain relatively stable when compared to ordinary least squares specifications.
Disciplines :
Finance
Author, co-author :
Coën, Alain; Université du Québec à Montréal > Graduate School of Business
Hübner, Georges ; Université de Liège - ULiège > HEC-Ecole de gestion : UER > Gestion financière
Desfleurs, Aurélie; Université du Québec en Outaouais - UQO > Department of Accounting
Language :
English
Title :
Hedge fund return specification with errors-in-variables
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We would like to mention that the Dagenais and Dagenais HME was developed for linear regressions. Thus, we have chosen to ignore nonlinear regressions and add optional factors as suggested by Agarwal and Naik. 9 Estimation for nonlinear models would be different and this is not the purpose of our study.
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The peaking monthly 1.335 per cent versus 0.338 per cent for the Global strategy is a clear outlier with respect to the rest of the table. This is probably due to the fact that this strategy mostly consists of dead funds, as the funds belonging to this strategy have been reshuffled to the other 'Global-based' strategies since 1999 (see Capocci and Hübner 33).
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