Mutual Fund Daily Conditional Performance
AbstractAbstract The empirical finance literature reveals that conditional models estimated with monthly data generally improve fund performance. Furthermore, it has been shown that using daily instead of monthly returns in an unconditional framework increases the proportion of abnormal performances relative to timing. In this article, we study conditional performance estimated with daily data in a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) framework. Our daily conditional alphas and global performances with GARCH are significantly better than those estimated with other parametrizations and they persist over time. Finally, the proportion of abnormal timing performances diminishes significantly when conditional parametrizations are used. Copyright (c) 2009 The Southern Finance Association and the Southwestern Finance Association.
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Bibliographic InfoArticle provided by Southern Finance Association & Southwestern Finance Association in its journal Journal of Financial Research.
Volume (Year): 32 (2009)
Issue (Month): 2 ()
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