Utilizing a machine learning technique known as random forests, we study whether regional output growth uncertainty helps to improve the accuracy of forecasts of regional output growth for 12 regions of the UK using monthly data for the period from 1970 to 2020. We use a stochastic volatility...

We examine the predictive value of El Niño and La Niña weather episodes for the subsequent realized variance of 16 agricultural commodity prices. To this end, we use high‐frequency data covering the period from 2009 to 2020 to estimate the realized variance along realized skewness, realized...

We examined the link between international equity flows and US stock returns. Based on the results of tests of in-sample and out-of-sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous...