Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark introduces biases in the measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence in mutual fund performance evaluation utilizing stock selection and timing measures that address these biases. We introduce a new factor exposure based approach for measuring the -static and dynamic -timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be overstating lack of skill because they ignore the managers' self-reported benchmark in the performance evaluation process. Revisiting Mutual Fund Performance Evaluation AbstractMutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark introduces biases in the measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence in mutual fund performance evaluation utilizing stock selection and timing measures that address these biases. We introduce a new factor exposure based approach for measuring the -static and dynamic -timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be overstating lack of skill because they ignore the managers' self-reported benchmark in the performance evaluation process.

We provide evidence using data from the G7 countries suggesting that return dispersion may serve as an economic state variable in that it reliably predicts time-variation in economic activity, market returns, the value and momentum premia and market volatility.A relatively high return dispersion predicts a deterioration in business conditions, a higher value premium, a smaller momentum premium and lower market returns.Dispersion based market and factor timing strategies outperform out-of-sample buy and hold strategies. The evidence are robust to alternative specifications of return dispersion and are not driven by US data. Return dispersion conveys incremental information relative to idiosyncratic risk.

No abstract

Restates the importance of asset volatility forecasts for option pricing and portfolio management and outlines previous research on forecasting models. Discusses the relative information content and predictive power of implied and historical volatility and the existence of overreaction in option markets. Analyses 1989‐1997 daily exchange rate data for six currencies to examine this. Presents the results, which suggest that implied volatility has more information than volatility based on past prices; and is better than GARCH‐based or historic volatility forecasts for horizons up to three months; but can be a biased predictor of future realized volatility. Finds limited evidence that long term volatilities in option prices overreact to short term volatilities.

No abstract