In the past twenty years, measures of economic uncertainty have been developed that are either purely market price-based, structural model-based using data on real fundamentals and asset prices, text-based, or survey-based. We compare the performance of these uncertainty measures in forecasting...

This paper derives ex-ante standard errors of risk premium predictions from neural networks (NNs). Considering standard errors, I provide improved investment strategies and ex-post out-of-sample (OOS) statistical inferences relative to existing literature. The equal-weighted (value-weighted)...

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so-called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions....

Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities...

We propose a new measure of the expected variance risk premium that is based on a forecast of the conditional variance from a GARCH-MIDAS model. We find that the new measure has strong predictive ability for future U.S. aggregate stock market returns and rationalize this result by showing that...

The financial market presents non-linearities for the behavior of stock returns for periods of high and low market. This article studies portfolios whose variance-covariance matrices are estimates using a multivariate model with regime change. Investment strategies for portfolios are presented...