We evaluate the performance of an extensive family of ARCH models in modeling the daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.

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.

Volatility prediction is the key variable in forecasting the prices of options, value-at-risk and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of asset returns, but also intra-day models, we were able to investigate their forecasting performance for three European equity indices. A consistent relation is shown between the examined models and the specific purpose of volatility forecasts. Although researchers cannot apply one model for all forecasting purposes, evidence in favor of models that are based on inter-day datasets when their criteria based on daily frequency, such as value-at-risk and forecasts of option prices, are provided.