We propose a novel empirical strategy that enables the researcher to select buckets of volatility forecasting models with similar accuracy. Using our Multiple Hypothesis Testing framework with False Discovery Rate (FDR) approach, we are able to identify buckets of more accurate models relative...

Bank failure prediction is a popular topic that requires highly accurate results. We contribute to the literature by determining whether models based on the crisis data are suitable for predicting bank failure during a stable period and which predictors can be held for long-term forecasting. In...

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion....

We test whether three well-known commodity-specific variables (basis, hedgingpressure, and momentum) may improve the predictive power for commodity futuresreturns of models otherwise based on macroeconomic factors. We compute recursive,out-of-sample forecasts for fifteen monthly commodity...