Examination over multiple horizons has been a routine in testing asset return predictability in finance and macroeconomics. In a simple predictive regression model, we find that the popular scaled test for multiple-horizon predictability has zero null rejection rate if the forecast horizon...

This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a...

We develop an exact and distribution-free procedure to test for quantile predictability at several quantile levels jointly, while allowing for an endogenous predictive regressor with any degree of persistence. The approach proceeds by combining together the quantile regression t-statistics from...

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural...