We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is generally accepted that forecast aggregation is...

The issue of forecast aggregation is to determine whether it is better to forecast a series directly or instead construct forecasts of its components and then sum these component forecasts. Notwithstanding some underlying theoretical results, it is generally accepted that forecast aggregation is...

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend...

This paper develops alternative text-based indexes assessing human sentiment and economic uncertainty in the oil market. The text analysis includes the titles and full articles of 138,797 oil related news items which featured in The Financial Times, Thompson-Reuters and The Independent from...

Most macroeconomic indicators failed to capture the sharp economic fluctuations during the Corona crisis in a timely manner. Instead, alternative high-frequency data have been used, aiming to monitor the economic situation. However, these data are often only loosely related to the business cycle...

In this paper, we propose a method for jointly estimating indexes of economic and financial conditions by exploiting the intertemporal link between their cyclical behavior. This method combines a dynamic factor model for the joint modeling of economic and financial variables with mixed...

This paper presents a weekly GDP indicator for Switzerland, which addresses the limitations of existing economic activity indicators using alternative high-frequency data created in the wake of the COVID-19 pandemic. The indicator is obtained from a Bayesian mixed-frequency dynamic factor model...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...

This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...