Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlation forecasts are affected by model uncertainty, the sources of which can include uncertainty about changing fundamentals and associated...

Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlations forecasts are affected by model uncertainty, sources of which can include uncertainty about changing fundamentals and associated...

This paper computes parametric estimates of a time-varying risk premium model and compares the one-step-ahead forecasts implied by that model with those given by a nonparametric kernel estimator of the conditional mean function. The conditioning information used for the nonparametric analysis is...

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term...

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts....