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MUTUAL FUND DAILY CONDITIONAL PERFORMANCE
Author(s) -
Coggins Frank,
Beaulieu MarieClaude,
Gendron Michel
Publication year - 2009
Publication title -
journal of financial research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.319
H-Index - 49
eISSN - 1475-6803
pISSN - 0270-2592
DOI - 10.1111/j.1475-6803.2009.01244.x
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , heteroscedasticity , bivariate analysis , autoregressive model , conditional variance , economics , statistics , mathematics , volatility (finance)
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.