We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our...

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...

We report results on the ex ante predictability of monthly excess stock returns in Germany using real-time and revised macroeconomic data. Our real-time macroeconomic data cover the period 1994-2005. We report that the contribution of real-time macroeconomic data to ex ante stock return...

We compare forecasts of stock market volatility based on real-time and revised macroeconomic data. To this end, we use a new dataset on monthly real-time macroeconomic variables for Germany. The dataset covers the period 1994-2005. We use statistical criteria, a utility-based criterion, and an...

We use a real-time forecasting approach to study the predictability of excess returns on a benchmark Euro Area real-estate index. The real-time forecasting approach accounts for the fact that, in real time, an investor forecasts returns under conditions of model instability and model...

We show how agents rational expectations regarding the state-contingent activation of policy instruments do not only impact the asset price it is designed to affect but spill over onto the entire range of asset prices in an economy. We present an application to exchange rate target zones as a...

We investigate the impact of good and bad news on stock market volatility. To this end, we utilize a novel data set of banks’ buy and sell recommendations for the German DAX30 stock market index and estimate an EGARCH(1,1) model which features these recommendations as well as several other...