Consensus professional forecasts of stock returns are three times more volatile than those of non-professionals and econometricians. This "excess" volatility in professional forecasts is not due to noise. Rather, professional forecasts respond immediately, strongly, and countercyclically to...

"We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in...

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return...

We use a parametric portfolio approach to estimate optimal commercial real estate portfolio policies. We do so using the NCREIF data set of commercial properties over the sample period 1984:Q2 to 2009:Q1. The richness of this extensive data set and the flexibility of the parametric portfolio...

We use a quantile-based measure of conditional skewness (or asymmetry) that is robust to outliers and therefore particularly suited for recalcitrant series such as emerging market returns. Our study is on the following portfolio returns: developed markets, emerging markets, the world, and...